The primary purpose of software estimation is not to predict a project’s outcome; it is to determine whether a project’s targets are realistic enough to allow the project to be controlled to meet them ‒ Steve McConnell
The primary purpose of software estimation is not to predict a project’s outcome; it is to determine whether a project’s targets are realistic enough to allow the project to be controlled to meet them ‒ Steve McConnell
Making decision in the presence of uncertainty requires estimating the impact of the outcome of those decisions. Here;s a collection of resources can can be used to guide that process
Overview of Software Development Life Cycle Models. Why traditional parametric estimating tools do not help estimate a software project developed using the Agile model. Explain and demonstrate the “nearest neighbor” analogy technique to estimate Agile software projects. Data and actions needed to implement the nearest neighbor technique
What is Software project management?? , What is a Project?, What is a Product?, What is Project Management?, What is Software Project Life Cycle?, What is a Product Life Cycle?, Software Project, Software Triple Constraints, Software Project Manager, Project Planning,
Building credible cost and schedule estimates requires discipline, skill, and experience. All 3 can be acquired over time. The starting point is understanding what processes make up the discipline of estimating
The primary purpose of software estimation is not to predict a project’s outcome; it is to determine whether a project’s targets are realistic enough to allow the project to be controlled to meet them ‒ Steve McConnell
Making decision in the presence of uncertainty requires estimating the impact of the outcome of those decisions. Here;s a collection of resources can can be used to guide that process
Overview of Software Development Life Cycle Models. Why traditional parametric estimating tools do not help estimate a software project developed using the Agile model. Explain and demonstrate the “nearest neighbor” analogy technique to estimate Agile software projects. Data and actions needed to implement the nearest neighbor technique
What is Software project management?? , What is a Project?, What is a Product?, What is Project Management?, What is Software Project Life Cycle?, What is a Product Life Cycle?, Software Project, Software Triple Constraints, Software Project Manager, Project Planning,
Building credible cost and schedule estimates requires discipline, skill, and experience. All 3 can be acquired over time. The starting point is understanding what processes make up the discipline of estimating
A study of the constraints affecting the proper utilization of computer appli...eSAT Journals
Abstract
Construction is one of the area in which computer application software are highly required to perform different task at various stages of project construction. Computers have been used to enhance the effectiveness of construction management. Efficient utilization of computer application software is a key to enhancing the proper management of construction activities which will contribute to the successful implementation of construction project. Careful selection of computer application software is required by the construction companies for proper management of their resources. The difficulty about computerization especially with regard to the use of computer application software in resource management require the knowledge and expertise of those working in construction companies, and who are directly involved in the management of construction resources. The study was carried out to determine the obstacles confronting the proper utilization of computer application software in resource management. The result of the questionnaire survey used in this research work clearly indicated those constraints, which include, non-understanding about software potentials, lack of qualified personnel to use the software, unaware of most of the resource management software available in the market, communication gap between the vendors and users which contribute to non-understanding of the full scope of the software, greater-know-how required from staff, among numerous others. A total of 22 construction companies were selected for the study. An open ended questionnaire which consist of 22 questions with regard to the constraints affecting the proper utilization of computer application software in resource management was designed, validated, and distributed among Engineers, Project Managers, as well as executives who are directly involved in the activities of the construction site.
Keywords: resource management, application software, construction management
EARLY STAGE SOFTWARE DEVELOPMENT EFFORT ESTIMATIONS – MAMDANI FIS VS NEURAL N...cscpconf
Accurately estimating the software size, cost, effort and schedule is probably the biggest
challenge facing software developers today. It has major implications for the management of
software development because both the overestimates and underestimates have direct impact for
causing damage to software companies. Lot of models have been proposed over the years by
various researchers for carrying out effort estimations. Also some of the studies for early stage
effort estimations suggest the importance of early estimations. New paradigms offer alternatives
to estimate the software development effort, in particular the Computational Intelligence (CI)
that exploits mechanisms of interaction between humans and processes domain
knowledge with the intention of building intelligent systems (IS). Among IS,
Artificial Neural Network and Fuzzy Logic are the two most popular soft computing techniques
for software development effort estimation. In this paper neural network models and Mamdani
FIS model have been used to predict the early stage effort estimations using the student dataset.
It has been found that Mamdani FIS was able to predict the early stage efforts more efficiently in
comparison to the neural network models based models.
Forecasting cost and schedule performanceGlen Alleman
For credible decisions to be made, we need confidence intervals on all the numbers we use to make decisions.
These confidence intervals come from the underlying statistics and the related probabilities.
Statistical forecasting, using time series analysis of past performance, is mandatory for any credible discussion of project performance in the future.
Introduction to monte-carlo analysis for software development - Troy Magennis...Troy Magennis
Forecasting and managing software development project risks & uncertainty. Monte-carlo analysis is the tool of choice for managing risk in many fields where risk is an inherent part of doing business. This paper examines how to use monte-carlo techniques to understand and leverage risk in Software Development projects and teams.
Integration of technical development within complex project environmentJacobs Engineering
This project will attempt to address this deficit. The high-level objectives are to develop a framework to facilitate better planning, monitoring and control of technical activities. This will be achieved through the identification, development and integration of suitable existing concepts and techniques into a complexity management framework. This should apply to all complex projects and particularly those relating to safety critical engineering projects. Primarily, the project builds upon the research which has been previously undertaken by this author in project failings, complexity and existing tools and techniques.
Agent-oriented software engineering is a promising new approach
to software engineering that uses the notion of an agent as the
primary entity of abstraction. The development of methodologies
for agent-oriented software engineering is an area that is currently
receiving much attention, there have been several agent-oriented
methodologies proposed recently and survey papers are starting to
appear. However the authors feel that there is still much work
necessary in this area; current methodologies can be improved
upon. This paper presents a new methodology, the Styx Agent
Methodology, which guides the development of collaborative
agent systems from the analysis phase through to system
implementation and maintenance. A distinguishing feature of
Styx is that it covers a wider range of software development lifecycle
activities than do other recently proposed agent-oriented
methodologies. The key areas covered by this methodology are
the specification of communication concepts, inter-agent
communication and each agent's behaviour activation—but it
does not address the development of application-specific parts of
a sys-tem. It will be supported by a software tool which is
currently in development.
Productivity Factors in Software Development for PC PlatformIJERA Editor
Identifying the most relevant factors influencing project performance is essential for implementing business strategies by selecting and adjusting proper improvement activities. The two major classification algorithms CRT and ANN that were recommended by the Auto Classifier tool in SPSS Modeler used for determining the most important variables (attributes) of software development in PC environment. While the accuracy of classification of productive versus non-productive cases are relatively close (72% vs 69%), their ranking of important variables are different. CRT ranks the Programming Language as the most important variable and Function Points as the least important. On the other hand, ANN ranks the Function Points as the most important followed by team size and Programming Language.
Traditional project management methods are based on scientific principles considered “normal science,” but lack a theoretical basis for this approach. These principles make use of linear step–wise refinement of the project management processes using a planning–as–management paradigm. Plans made in this paradigm are adjusted by linear feedback methods. These plans cannot cope with the multiple interacting and continuously changing technology and market forces. They behave as a linear, deterministic, Closed–Loop control system.
Definition of Project, Difference between Project and Program, PMLC, Project Management Life Cycle, Project Manager Vs Line Managers, Challenges in International Projects
The resources listed here are the starting point for anyone interested in applying the principles developed in this briefing for integrating Agile with Earned Value Management projects
The most important aim of software engineering is to improve software productivity and quality of software product and further reduce the cost of software and time using engineering and management techniques.Broadly speaking, software engineering initiative has been introduced during software crisis period to describe the collection of techniques that apply engineering and management skills to the construction and
support of software process and products. There is no universally agreed theory for software measurement and the software metrics are useful for obtaining the information on evaluation of process and product in software engineering. It helps to plan and carry out improvement in software organizations and to provide objective information about project performance, process capability and product quality. The process capability is extremely important for software industry because the quality of products is largely determined by the quality of the processes. The make use of of existing metrics and development of innovative software metrics will be important factors in future software engineering process and product development. In future, research work will be based on using software metrics in software development for the development of the time schedule, cost estimates and software quality and can be improved through software metrics. The permanent application of measurement based methodologies is used to the software process and its products to provide important and timely management information, together with the use of those techniques to improve that software process and its products. This research paper mainly concentrates on the overview of unique basics of software measurement and exclusive fundamentals of software metrics in software engineering.
A study of the constraints affecting the proper utilization of computer appli...eSAT Journals
Abstract
Construction is one of the area in which computer application software are highly required to perform different task at various stages of project construction. Computers have been used to enhance the effectiveness of construction management. Efficient utilization of computer application software is a key to enhancing the proper management of construction activities which will contribute to the successful implementation of construction project. Careful selection of computer application software is required by the construction companies for proper management of their resources. The difficulty about computerization especially with regard to the use of computer application software in resource management require the knowledge and expertise of those working in construction companies, and who are directly involved in the management of construction resources. The study was carried out to determine the obstacles confronting the proper utilization of computer application software in resource management. The result of the questionnaire survey used in this research work clearly indicated those constraints, which include, non-understanding about software potentials, lack of qualified personnel to use the software, unaware of most of the resource management software available in the market, communication gap between the vendors and users which contribute to non-understanding of the full scope of the software, greater-know-how required from staff, among numerous others. A total of 22 construction companies were selected for the study. An open ended questionnaire which consist of 22 questions with regard to the constraints affecting the proper utilization of computer application software in resource management was designed, validated, and distributed among Engineers, Project Managers, as well as executives who are directly involved in the activities of the construction site.
Keywords: resource management, application software, construction management
EARLY STAGE SOFTWARE DEVELOPMENT EFFORT ESTIMATIONS – MAMDANI FIS VS NEURAL N...cscpconf
Accurately estimating the software size, cost, effort and schedule is probably the biggest
challenge facing software developers today. It has major implications for the management of
software development because both the overestimates and underestimates have direct impact for
causing damage to software companies. Lot of models have been proposed over the years by
various researchers for carrying out effort estimations. Also some of the studies for early stage
effort estimations suggest the importance of early estimations. New paradigms offer alternatives
to estimate the software development effort, in particular the Computational Intelligence (CI)
that exploits mechanisms of interaction between humans and processes domain
knowledge with the intention of building intelligent systems (IS). Among IS,
Artificial Neural Network and Fuzzy Logic are the two most popular soft computing techniques
for software development effort estimation. In this paper neural network models and Mamdani
FIS model have been used to predict the early stage effort estimations using the student dataset.
It has been found that Mamdani FIS was able to predict the early stage efforts more efficiently in
comparison to the neural network models based models.
Forecasting cost and schedule performanceGlen Alleman
For credible decisions to be made, we need confidence intervals on all the numbers we use to make decisions.
These confidence intervals come from the underlying statistics and the related probabilities.
Statistical forecasting, using time series analysis of past performance, is mandatory for any credible discussion of project performance in the future.
Introduction to monte-carlo analysis for software development - Troy Magennis...Troy Magennis
Forecasting and managing software development project risks & uncertainty. Monte-carlo analysis is the tool of choice for managing risk in many fields where risk is an inherent part of doing business. This paper examines how to use monte-carlo techniques to understand and leverage risk in Software Development projects and teams.
Integration of technical development within complex project environmentJacobs Engineering
This project will attempt to address this deficit. The high-level objectives are to develop a framework to facilitate better planning, monitoring and control of technical activities. This will be achieved through the identification, development and integration of suitable existing concepts and techniques into a complexity management framework. This should apply to all complex projects and particularly those relating to safety critical engineering projects. Primarily, the project builds upon the research which has been previously undertaken by this author in project failings, complexity and existing tools and techniques.
Agent-oriented software engineering is a promising new approach
to software engineering that uses the notion of an agent as the
primary entity of abstraction. The development of methodologies
for agent-oriented software engineering is an area that is currently
receiving much attention, there have been several agent-oriented
methodologies proposed recently and survey papers are starting to
appear. However the authors feel that there is still much work
necessary in this area; current methodologies can be improved
upon. This paper presents a new methodology, the Styx Agent
Methodology, which guides the development of collaborative
agent systems from the analysis phase through to system
implementation and maintenance. A distinguishing feature of
Styx is that it covers a wider range of software development lifecycle
activities than do other recently proposed agent-oriented
methodologies. The key areas covered by this methodology are
the specification of communication concepts, inter-agent
communication and each agent's behaviour activation—but it
does not address the development of application-specific parts of
a sys-tem. It will be supported by a software tool which is
currently in development.
Productivity Factors in Software Development for PC PlatformIJERA Editor
Identifying the most relevant factors influencing project performance is essential for implementing business strategies by selecting and adjusting proper improvement activities. The two major classification algorithms CRT and ANN that were recommended by the Auto Classifier tool in SPSS Modeler used for determining the most important variables (attributes) of software development in PC environment. While the accuracy of classification of productive versus non-productive cases are relatively close (72% vs 69%), their ranking of important variables are different. CRT ranks the Programming Language as the most important variable and Function Points as the least important. On the other hand, ANN ranks the Function Points as the most important followed by team size and Programming Language.
Traditional project management methods are based on scientific principles considered “normal science,” but lack a theoretical basis for this approach. These principles make use of linear step–wise refinement of the project management processes using a planning–as–management paradigm. Plans made in this paradigm are adjusted by linear feedback methods. These plans cannot cope with the multiple interacting and continuously changing technology and market forces. They behave as a linear, deterministic, Closed–Loop control system.
Definition of Project, Difference between Project and Program, PMLC, Project Management Life Cycle, Project Manager Vs Line Managers, Challenges in International Projects
The resources listed here are the starting point for anyone interested in applying the principles developed in this briefing for integrating Agile with Earned Value Management projects
The most important aim of software engineering is to improve software productivity and quality of software product and further reduce the cost of software and time using engineering and management techniques.Broadly speaking, software engineering initiative has been introduced during software crisis period to describe the collection of techniques that apply engineering and management skills to the construction and
support of software process and products. There is no universally agreed theory for software measurement and the software metrics are useful for obtaining the information on evaluation of process and product in software engineering. It helps to plan and carry out improvement in software organizations and to provide objective information about project performance, process capability and product quality. The process capability is extremely important for software industry because the quality of products is largely determined by the quality of the processes. The make use of of existing metrics and development of innovative software metrics will be important factors in future software engineering process and product development. In future, research work will be based on using software metrics in software development for the development of the time schedule, cost estimates and software quality and can be improved through software metrics. The permanent application of measurement based methodologies is used to the software process and its products to provide important and timely management information, together with the use of those techniques to improve that software process and its products. This research paper mainly concentrates on the overview of unique basics of software measurement and exclusive fundamentals of software metrics in software engineering.
End-User Development for Artificial Intelligence: A Systematic Literature ReviewAndrea Esposito
Slides for the homonymous paper, presented at IS-EUD 2023.
Full paper: https://doi.org/10.1007/978-3-031-34433-6_2
Free access PDF: https://rdcu.be/dd32o
Abstract:
In recent years, Artificial Intelligence has become more and more relevant in our society. Creating AI systems is almost always the prerogative of IT and AI experts. However, users may need to create intelligent solutions tailored to their specific needs. In this way, AI systems can be enhanced if new approaches are devised to allow non-technical users to be directly involved in the definition and personalization of AI technologies. End-User Development (EUD) can provide a solution to these problems, allowing people to create, customize, or adapt AI-based systems to their own needs. This paper presents a systematic literature review that aims to shed the light on the current landscape of EUD for AI systems, i.e., how users, even without skills in AI and/or programming, can customize the AI behavior to their needs. This study also discusses the current challenges of EUD for AI, the potential benefits, and the future implications of integrating EUD into the overall AI development process.
Identification, Analysis & Empirical Validation (IAV) of Object Oriented Desi...rahulmonikasharma
Metrics and Measure are closely inter-related to each other. Measure is defined as way of defining amount, dimension, capacity or size of some attribute of a product in quantitative manner while Metric is unit used for measuring attribute. Software quality is one of the major concerns that need to be addressed and measured. Object oriented (OO) systems require effective metrics to assess quality of software. The paper is designed to identify attributes and measures that can help in determining and affecting quality attributes. The paper conducts empirical study by taking public dataset KC1 from NASA project database. It is validated by applying statistical techniques like correlation analysis and regression analysis. After analysis of data, it is found that metrics SLOC, RFC, WMC and CBO are significant and treated as quality indicators while metrics DIT and NOC are not significant. The results produced from them throws significant impact on improving software quality.
Quality Factor between Visual Studio and Android Studio for Developing Video ...ijtsrd
"In this paper, research of a video control, based on hand gesture which recognizes a play and pause, is proposed. In the field of image processing, being able to recognize human gesture in daily life application is one of the most appealing technology as it represents the communication between human and machine. Comparative tools such as Visual Studio VS and Android Studio AS can be used in developing the video control based on hand gestures. A comparison that will be discussed in this paper is based on the quality factor which is the simplicity for each of the software proposed. Siti Norhidayah Aman | Noor Zuhaili Md Yasin | Illiana Azizan ""Quality Factor between Visual Studio and Android Studio for Developing Video Control Based on Hand Gestures"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Special Issue | International Conference on Advanced Engineering and Information Technology , November 2018, URL: https://www.ijtsrd.com/papers/ijtsrd19134.pdf
Paper URL: https://www.ijtsrd.com/computer-science/programming-language/19134/quality-factor-between-visual-studio-and-android-studio-for-developing-video-control-based-on-hand-gestures/siti-norhidayah-aman"
Improving Effort Estimation in Agile Software Development ProjectsGedi Siuskus
A key principle in agile software development is to manage changing user needs at different phases of the software project development cycle. It splits the development into smaller iterations (sprints) to keep both developers and customers focused on one of them at the time. By planning and working on small consecutive iterations agile teams reduce uncertainty of changing user needs. However this approach has its drawbacks too. It becomes hard for agile team to plan and estimate the whole project in advance accurately as not much information is available. Therefore agile project planning turns into guesstimation of the effort required. It is based on available information about the system requirements and resources available. This paper proposes a method to improve the agile effort guesstimation by applying functional analysis to size user stories. A number of user stories from a media company are obtained to conduct the case study. The COSMIC method is used to size the user stories in functional points. Next those measurements are later applied to calculate the final project effort. The case study concludes that COSMIC user requirements sizing method can improve effort estimation and benefit agile teams in planning projects.
Keywords: agile, effort estimation, user story, function points, COSMIC.
Graphical controls based environment for user interface evaluationS. Charfi
For more than two decades, the HCI community has elaborated numerous tools for user interface evaluation. Although the related tools are wide, the evaluation remains a difficult task. This paper presents a new approach for user interface evaluation. The proposed evaluation process focuses on utility and usability as software quality factors. It is based on the UI ergonomic quality inspection as well as the analysis and the study of the Human-Computer interaction. The proposed approach is mainly based on graphic controls dedicated to the user interface evaluation. These controls have, on the one hand, the role to compose graphically the interfaces. On the other hand, they contribute to the UI evaluation through integrated mechanisms. The evaluation is structured into two phases. The first consists of a local self-evaluation of the graphical controls according to a set of ergonomic guidelines. This set is specified by the evaluator. The second allows an electronic informer to estimate the interaction between the user interface (graphically composed by the evaluation based controls) and the user.
Evaluating effectiveness factor of object oriented design a testability pers...ijseajournal
Effectiveness is important quality factor to testability measurement of object oriented software at an initial
stage of software development process exclusively at design phase for high quality product. It will help
developer’s design capability to achieve the specified functionalities, characteristics, better design quality
and behavior using appropriate object oriented design (OOD) concepts and procedures. Metric based
model for ‘Effectiveness Quantification Model of Object Oriented Design’ has been proposed by
establishing the correlation between effectiveness and OOD constructs. Later ‘Effectiveness Quantification
Model’ is empirically validated and statistical significance of the study considers the high correlation for
model acceptance. The aim of this research work is to encourage researchers and developers for inclusion
of the effectiveness quantification model to access and quantify software effectiveness quality factor at
design time.
Planning projects usually starts with tasks and milestones. The planner gathers this information from the participants – customers, engineers, subject matter experts. This information is usually arranged in the form of activities and milestones. PMBOK defines “project time management” in this manner. The activities are then sequenced according to the projects needs and mandatory dependencies.
Increasing the Probability of Project SuccessGlen Alleman
Risk Management is essential for development and production programs. Information about key cost, performance and schedule attributes are often uncertain or unknown until late in the program.
Risk issues that can be identified early in the program, which may potentially impact the program, termed Known Unknowns, can be alleviated with good risk management. -- Effective Risk Management 2nd Edition, Page 1, Edmund Conrow, American Institute of Aeronautics and Astronautics, 2003
Cost and schedule growth for complex projects is created when unrealistic technical performance expectations, unrealistic cost and schedule estimates, inadequate risk assessments, unanticipated technical issues, and poorly performed and ineffective risk management, contribute to project technical and programmatic shortfalls
From Principles to Strategies for Systems EngineeringGlen Alleman
From Principles to Strategies How to apply Principles, Practices, and Processes of Systems Engineering to solve complex technical, operational,
and organizational problems
Building a Credible Performance Measurement BaselineGlen Alleman
Establishing a credible Performance Measurement Baseline, with a risk adjusted Integrated Master Plan and Integrated Master Schedule, starts with the WBS and connects Technical Measures of progress to Earned Value
Capabilities‒Based Planning the capabilities needed to accomplish a mission or fulfill a business strategy
Only when capabilities are defined can we start with requirements elicitation
Starting with the development of a Rough Order of Magnitude (ROM) estimate of work and duration, creating the Product Roadmap and Release Plan, the Product and Sprint Backlogs, executing and statusing the Sprint, and informing the Earned Value Management Systems, using Physical Percent Complete of progress to plan.
Program Management Office Lean Software Development and Six SigmaGlen Alleman
Successfully combining a PMO, Agile, and Lean / 6 starts with understanding what benefit each paradigm brings to the table. Architecting a solution for the enterprise requires assembling a “Systems” with processes, people, and principles – all sharing the goal of business improvement.
This resource document describes the Program Governance Road map for product development, deployment, and sustainment of products and services in compliance with CMS guidance, ITIL IT management, CMMI best practices, and other guidance to assure high quality software is deployed for sustained operational success in mission critical domains.
Removing Uninteresting Bytes in Software FuzzingAftab Hussain
Imagine a world where software fuzzing, the process of mutating bytes in test seeds to uncover hidden and erroneous program behaviors, becomes faster and more effective. A lot depends on the initial seeds, which can significantly dictate the trajectory of a fuzzing campaign, particularly in terms of how long it takes to uncover interesting behaviour in your code. We introduce DIAR, a technique designed to speedup fuzzing campaigns by pinpointing and eliminating those uninteresting bytes in the seeds. Picture this: instead of wasting valuable resources on meaningless mutations in large, bloated seeds, DIAR removes the unnecessary bytes, streamlining the entire process.
In this work, we equipped AFL, a popular fuzzer, with DIAR and examined two critical Linux libraries -- Libxml's xmllint, a tool for parsing xml documents, and Binutil's readelf, an essential debugging and security analysis command-line tool used to display detailed information about ELF (Executable and Linkable Format). Our preliminary results show that AFL+DIAR does not only discover new paths more quickly but also achieves higher coverage overall. This work thus showcases how starting with lean and optimized seeds can lead to faster, more comprehensive fuzzing campaigns -- and DIAR helps you find such seeds.
- These are slides of the talk given at IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW 2022.
In his public lecture, Christian Timmerer provides insights into the fascinating history of video streaming, starting from its humble beginnings before YouTube to the groundbreaking technologies that now dominate platforms like Netflix and ORF ON. Timmerer also presents provocative contributions of his own that have significantly influenced the industry. He concludes by looking at future challenges and invites the audience to join in a discussion.
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
UiPath Test Automation using UiPath Test Suite series, part 5DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 5. In this session, we will cover CI/CD with devops.
Topics covered:
CI/CD with in UiPath
End-to-end overview of CI/CD pipeline with Azure devops
Speaker:
Lyndsey Byblow, Test Suite Sales Engineer @ UiPath, Inc.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
SAP Sapphire 2024 - ASUG301 building better apps with SAP Fiori.pdfPeter Spielvogel
Building better applications for business users with SAP Fiori.
• What is SAP Fiori and why it matters to you
• How a better user experience drives measurable business benefits
• How to get started with SAP Fiori today
• How SAP Fiori elements accelerates application development
• How SAP Build Code includes SAP Fiori tools and other generative artificial intelligence capabilities
• How SAP Fiori paves the way for using AI in SAP apps
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
GridMate - End to end testing is a critical piece to ensure quality and avoid...ThomasParaiso2
End to end testing is a critical piece to ensure quality and avoid regressions. In this session, we share our journey building an E2E testing pipeline for GridMate components (LWC and Aura) using Cypress, JSForce, FakerJS…
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
Pushing the limits of ePRTC: 100ns holdover for 100 daysAdtran
At WSTS 2024, Alon Stern explored the topic of parametric holdover and explained how recent research findings can be implemented in real-world PNT networks to achieve 100 nanoseconds of accuracy for up to 100 days.
Climate Impact of Software Testing at Nordic Testing DaysKari Kakkonen
My slides at Nordic Testing Days 6.6.2024
Climate impact / sustainability of software testing discussed on the talk. ICT and testing must carry their part of global responsibility to help with the climat warming. We can minimize the carbon footprint but we can also have a carbon handprint, a positive impact on the climate. Quality characteristics can be added with sustainability, and then measured continuously. Test environments can be used less, and in smaller scale and on demand. Test techniques can be used in optimizing or minimizing number of tests. Test automation can be used to speed up testing.
Threats to mobile devices are more prevalent and increasing in scope and complexity. Users of mobile devices desire to take full advantage of the features
available on those devices, but many of the features provide convenience and capability but sacrifice security. This best practices guide outlines steps the users can take to better protect personal devices and information.
National Security Agency - NSA mobile device best practices
5.0 Estimating Agile Development Projects
1. Integration of Agile and Earned Value Management 30
5. Estimating Software Intensive System of Systems
The primary purpose of software estimation is not to predict a project’s outcome; it is to determine whether a
project’s targets are realistic enough to allow the project to be controlled to meet them ‒ Steve McConnell
Papers on Estimating Software Intensive System of Systems
630. “Easy Function Points – ‘Smart’ Approximation Technique for the IFPUG and COSMIC Methods,” Luca
Santillo, 2012 Joint Conference of the 22nd International Workshop on Software Measurement and the
2012 Seventh International Conference on Software Process and Product Measurement, 2012
631. “Automated Function Points: Critical Evaluation and Discussion,” Luigi Lavazza, IEEE/ACM International
Workshop on Emerging Trends in Software Metrics, 2015.
632. “A Software Maintenance Project Size Estimation Tool Based On Cosmic Full Function Point,” Chi-Jui Lin
and Dow-Ming Yeh, International Computer Symposium (ICS), 2016.
633. “An Empirical Study on the Estimation of Size and Complexity of Software Applications with Function
Points Analysis,” Luís M. Alves, Sérgio Oliveira, Pedro Ribeiro, and Ricardo J. Machado, 14
th
International
Conference on Computational Science and Its Applications, 2014
634. “Extending Function Point Analysis to Object-Oriented Requirements Specifications,” Vahan Harput,
Hermann Kaindl, and Stefan Kramer, 11
th
International Software Metrics Symposium, 2005
635. “Adapting Function Point Analysis to Estimate Data Mart Size,” Angélica Toffano Calazans, Káthia Marçal
de Oliveira, and Rildo Ribeiro dos Santos, 10
th
International Symposium on Software Metrics, 2004.
636. “The Internal Revenue Service Function Point Analysis Program,” Charles Tichenor, IEEE Computer
Software and Applications Conference, 1997
637. “Applications of Monte Carlo,” Herman Kahn, RM-1237-AEC, Atomic Energy Commission, 19 April 1954.
638. “Project Decision Analysis Process,” Lev Virine, Intaver Institute, January 2014
639. “Learning and Making Decisions When Costs and Probabilities are Both Unknown,” Bianca Zadrozny and
Charles Elkan, Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery
and Data Mining, KDD ’01, Pages 204-213, August 26 - 29, 2001
640. “A Novel Approach for Estimating Truck Factors,” Guilherme Avelino, Leonardo Passos, Andre Hora, and
Marco Tulio Valente, 24
th
International Conference on Program Comprehension (ICPC)
641. “Comparison among Methods of Modeling Epistemic Uncertainty in Reliability Estimation,” Nam Ho Kim,
Min Young Yoo and Joo-Ho Choi, The Eighth China-Japan-Korea Joint Symposium on Optimization of
Structural and Mechanical Systems, May 25-29, 2014.
642. “Uncertainty in Through-Life Costing—Review and Perspectives,” Yee Mey Goh, Linda B. Newnes, Antony
R. Mileham, Chris A. McMahon, and Mohammad E. Saravi, IEEE Transactions On Engineering
Management, Vol. 57, No. 4, November 2010.
643. “Probabilistic Economics,” John J. McCall, The Bell Journal of Economics and Management Science, Vol. 2,
No. 2 (Autumn, 1971), pp. 403-433
644. “A Review of Studies on Expert Estimation of Software Development Effort,” M. Jørgensen, Journal of
Systems and Software, Vol. 70, No. 1-2, pp. 37‒60, 2004.
645. “Advancement of decision-making in Agile Projects by applying Logistic Regression on Estimates,”
Lakshminarayana Kompella, CA Technologies, 13 August 2013.
646. “How to Estimate Software Size and Effort in Iterative Development,” Aleš Živkovič, Marjan Heričko, in
Information and Software Technology, vol. 50, issue. 7-8, pp. 772-781
647. “A case study on the conversion of Function Points into COSMIC,” Filomena Ferrucci, Carmine Gravino,
and Federica Sarro, 37th EUROMICRO Conference on Software Engineering and Advanced Applications
(SEAA), IEEE, 2011.
648. “How to Make Best Use of Cross-Company Data for Web Effort Estimation?” Leandro Minku, Federica
Sarro, Emilia Mendes, and Filomena Ferrucci, 2015 ACM/IEEE International Symposium on Empirical
Software Engineering and Measurement (ESEM), IEEE, 2015.
649. "Web effort estimation: function point analysis vs. COSMIC," Di Martino, Sergio, et al., Information and
Software Technology 72 (2016): 90-109.
2. Estimating Software Intensive System of Systems
Integration of Agile and Earned Value Management 31
650. “Conversion from IFPUG FPA to COSMIC: within- vs without-company equations,” Filomena Ferrucci,
Carmine Gravino, and Federica Sarro, Conference on Software Engineering and Advanced Applications
(SEAA), 2014 40th EUROMICRO, 2014
651. “From Function Points to COSMIC - A Transfer Learning Approach for Effort Estimation,” Anna Corazza,
Sergio Di Martino, Filomena Ferrucci, Carmine Gravino, Federica Sarro, International Conference on
Product-Focused Software Process Improvement, 2015
652. “Early and Extended Function Point: a new method for Function Points estimation,” Roberto Meli, IFPUG
Fall Conference, September 15-19, 1997
653. “An estimation technique in agile archetype using story points and function point analysis,” Deepak
Kumar, Saru Dhir, V.B. Singh, International Journal of Process Management and Benchmarking, 7(4):518,
January 2017.
654. “User Story Software Estimation: A Simplification Of Software Estimation Model With Distributed Extreme
Programming Estimation Technique,” Ridi Ferdiana, Paulus Insap Santoso, Lukito Edi Nugroho, and Ahmad
Ashari, Scientific Journal of Information Technology, Vol. 9, No. 1, January 2011.
655. “Decision Making in Agile Development: A Focus Group Study of Decisions and Obstacles,” Meghann
Drury, Kieran Conboy, and Ken Power, IEEE Agile Conference, 2011.
656. “From Story Points to COSMIC Function Points From Story Points to COSMIC Function Points in Agile
Software Development – A Six Sigma perspective,” Thomas Fehlmann and Luca Santillo, MetriKon, 2010.
657. “Can Functional Size Measures Improve Effort Estimation in SCRUM?,” Valentina Lenarduzzi and Davide
Taibi, The Ninth International Conference on Software Engineering Advances (ICSEA 2014), 2014
658. “MVP Explained: A Systematic Mapping Study on the Definitions of Minimal Viable Product,” Valentina
Lenarduzzi and Davide Taibi, Euromicro SEAA, 2016
659. “A Decision Model for Estimating the Effort of Software Projects using Bayesian Theory,” Foad Marzoughi,
Mohammad Mehdi Farhangian, Ali Marzoughi, Alex Tze Hiang Sim, 2
nd
International Conference on
Software Technology and Engineering(ICSTE), 2010.
660. “Towards improving decision making and estimating the value of decisions in value-based software
engineering: the VALUE framework,” Emilia Mendes, Pilar Rodriguez, Vitor Freitas, Simon Baker and
Mohamed Amine Atoui, Software Quality Journal, 17 March 2017.
661. “A Probabilistic Model for Predicting Software Development Effort,” Parag C. Pendharkar, Girish H.
Subramanian, and James A. Rodger, IEEE Transactions On Software Engineering, Vol. 31, No. 7, July 2005
662. “A Software Size Estimation Method Based on Improved FPA,” Ya-fang Fu, Xiao-dong Liu, Ren-nong Yang,
DU Yi-lin, LI Yan-jie, Second WRI World Congress on Software Engineering, 2010
663. “An Empirical Study of eServices Product UML Sizing Metrics,” Yue Chen, Barry W. Boehm, Ray Madachy,
and Ricardo Valerdi,
664. “A Model-Based and Automated Approach to Size Estimation of Embedded Software Components,”
Kenneth Lind and Rogardt Heldal, MODELS 2011, LNCS 6981, J. Whittle, T. Clark, and T. Kühne (Eds.), pp.
334–348, 2011.
665. “A Model-Based and Automated Approach to Size Estimation of Embedded Software Components,”
666. “Towards Component-Aware Function Point Measurement,” Luigi Lavazza, Valentina Lenarduzzi, and
Davide Taibi, Joint Conference of the International Workshop on Software Measurement and the
International Conference on Software Process and Product Measurement, 2016
667. “Historical Data Repositories in Software Engineering: Status and Possible Improvements, Luigi Lavazza
Luca Santillo, Joint Conference of the 22
nd
International Workshop on Software Measurement and the
2012 Seventh International, 2012.
668. Conference on Software Process and Product Measurement
669. “An Exploratory Study on Functional Size Measurement based on Code,” Hennie Huijgens, Magiel
Bruntink, Arie van Deursen, Tijs van der Storm, and Frank Vogelezang, IEEE/ACM International Conference
on Software and System Processes, 2016
670. “Simple Function Points for Effort Estimation: a Further Assessment,” Filomena Ferrucci, Carmine Gravino,
and Luigi Lavazza, 31
st
Annual ACM Symposium, SAC 2016, April 04-08, 2016.
671. “The Cone of Uncertainty,” Letters, IEEE Software
3. Estimating Software Intensive System of Systems
Integration of Agile and Earned Value Management 32
672. “Uncertainty in Through-Life Costing—Review and Perspectives,” Yee Mey Goh, Linda B. Newnes, Antony
R. Mileham, Chris A. McMahon, and Mohammad E. Saravi, IEEE Transactions On Engineering
Management, Vol. 57, No. 4, November 2010,
673. “Perspectives on prediction: Does third-person imagery improve task completion estimates?” Roger
Buehler, Dale Griffin, Kent C. H. Lam, Jennifer Deslauriers, Organizational Behavior and Human Decision
Processes, 117, 2012, pp. 138–149
674. “Exploring the "Planning Fallacy": Why People Underestimate Their Task Completion Times,” Roger
Buehler, Dale Wesley Griffin, and Michael Ross, Journal of Personality and Social Psychology
675. 1994, Vol. 67, No. 3.366-381.
676. “Using Real Options to Quantify Portfolio Value in Business Cases,” George O. Bayer Jr., ICEAA 2017
Professional Development & Training Workshop, March 27, 2017
677. “Adapting a classic Independent Cost Estimation (ICE) Cost Shop for Agile and DevOPS estimates,” David P.
Seaver, National Security Agency, Software & IT CAST Conference, August 2017.
678. “Bottom Up Methods of Estimating Software SEPM and Non-DCTI Cost,“ James R. Black, 2017 ICEAA
Professional Development & Training Workshop, June 2017
679. “Project Team Sizing and Cost Forecasting using Norden-Rayleigh Curves,” Alan R Jones, ACostE
Conference, 3 November 2011.
680. “Phase Distribution of Software Development Effort,” Ye Yang, Mei He, Mingshu Li, Q ing Wang, Barry
Boehm, ESEM’08, October 9-10, 2008.
681. “A Study on Software Development Month Effort,” Yinlong Liu, Yong Wang, Journal of Software, Volume
10, Number 7, July 2015.
682. “Software Engineering Economics,” Barry W. Boehm, CSSE USC Technical Report, 1984.
683. “The Evolution of Software Size: A Search for Value,” Arlene Minkiewicz, Cross Talk: The Journal of
Defense Software Engineering, March/April 2009.
684. “A Specific Effort Estimation Method Using Function Point,” Bingchiang Jeng, Dowming Yeh, Deron Wang,
Shu-Lan Chu, Journal Of Information Science And Engineering, 27, 1363-1376 (2011)
685. “A rule-based approach for estimating software development cost using function point and goal and
scenario based requirements,” Soonhwang Choi, Sooyong Park, and Vijayan Sugumaran, Expert Systems
with Applications 39(1):406-418 · January 2012
686. “Evaluation of Cost Estimation Metrics: Towards a Unified Terminology,” Izzat M. Alsmadi and Maryam S.
Nuser, Journal of Computing and Information Technology - CIT 21, 2013, 1, pp. 23–34
687. “Approximation of COSMIC functional size to support early effort estimation in Agile,” Ishrar Hussain, Leila
Kosseim, and Olga Ormandjieva, Data and Knowledge Engineering, 2012.
688. “Unskilled and Unaware of it: How Difficulties in Recognizing One’s Own Incompetence Lead to Inflated
Self-Assessments,” Justin Kruger and David Dunning, Journal of Personality and Social Psychology, 1999,
Vol. 77, No. 6, pp. 1212-1134.
689. “PCA based cost estimation model for agile software development projects,” Sakshi Garg and Daya Gupta,
2015 International Conference on Industrial Engineering and Operations Management (IEOM).
690. “A Proposed Model Of Agile Methodology In Software Development,” Anjali Sharma and Karambir,
International Journal Of Engineering Sciences & Research Technology, July 2016.
691. “Effort Estimation in Agile Global Software Development Context,” Ricardo Britto, Muhammad Usman,
and Emilia Mendes, Agile Methods. Large-Scale Development, Refactoring, Testing, and Estimation: XP
2014 International Workshops, Rome, Italy, May 26-30, 2014.
692. “Predicting development effort from user stories. In: Empirical software engineering and measurement
(ESEM),” Pekka Abrahamsson, Ilenia Fronza, Raimund Moser Jelena Vlasenko, and Witold Pedrycz, 2011
International Symposium on Empirical Software Engineering and Measurement,
693. “Empirical assessment of machine learning models for agile software development effort estimation using
story points,” Shashank Mouli Satapathy and Santanu Kumar Rath, Innovations in Systems and Software
Engineering, pp 1-10.
694. “How is Effort Estimated in Agile Software Development Projects?,” Tina Schweighofer, Andrej Kline, Luka
Pavlic, Marjan Hericko,
4. Estimating Software Intensive System of Systems
Integration of Agile and Earned Value Management 33
695. “A New Business Model For Function Point Metrics,” Capers Jones, Capers Jones and Associates, 8 May
2008,
696. “Function Point Estimation Methods: A Comparative Overview,” Roberto Meli and Luca Santillo
697. “Procedures and Software for Assessing Uncertainty in Cost Estimates,” Henry L. Eskew and Walter R.
Nunn, Center for Naval Analyses, CRM-95-87, June 1995.
698. “Cost Estimating Risk and Cost Estimating Uncertainty Guidelines,” Timothy P. Anderson and Jeffrey S.
Cherwonik
699. “The Trouble With Budgeting to the 80th Percentile,” Timothy P. Anderson, Washington Area Chapter of
SCEA, 15 November 2006.
700. “Space Systems Cost Risk Handbook Applying the Best Practices in Cost Risk Analysis to Space System Cost
Estimates,” Edited by Timothy P. Anderson and Raymond P. Covert, Space Systems Cost Analysis Group,
16 November 2005.
701. “A Collective Study of PCA and Neural Network based on COCOMO for Software Cost Estimation,” Rina M.
Waghmode, L.V. Patil, and S.D Joshi, International Journal of Computer Applications, Volume 74– No. 16,
July 2013.
702. “Integrating Risk Assessment with Cost Estimation,” Kari Känsälä, IEEE Software, May/June 1997.
703. “Valuation of Software Initiatives under Uncertainty: Concepts, Issues, and Techniques,” Hakan Erdogmus,
John Favaro and Michael Halling, VBSE 2005.
704. “Software economics: status and prospects,” Barry Boehm and Kevin Sullivan, Information and
Technology 41, 1999, pp. 937-946.
705. “Software Effort, Quality, and Cycle Time: A Study of CMM Level 5 Projects,” Manish Agrawal and Kaushal
Chari, IEEE Transactions On Software Engineering, Vol. 33, No. 3, March 2007.
706. “Estimating the development cost of custom software,” Ioannis Stamelosa, Lefteris Angelis, Maurizio
Morisio,Evaggelos Sakellaris, and George L. Bleris, Information & Management 40 (2003) pp. 729–741.
707. “Examining the Feasibility of a Case-Based Reasoning Model for Software Effort Estimation,” Tridas
Mukhopadhyay, Steven S. Vicinanza, Michael J. Prietula, International Conference on Information Systems,
Copenhagen, Denmark, December 1990.
708. “Estimating Software Based on Use Case Points,” Edward Carroll, OOPSLA’05, October 16–20, 2005.
709. “Comparison of Functional Size Based Estimation and Story Points, Based on Effort Estimation
Effectiveness in SCRUM Projects,” Erdir Ungan, Numan Çizmeli, and Onur Demirörs, 2014 40
th
Euromicro
Conference on Software Engineering and Advanced Applications, pp. 77-80.
710. “Approximate COSMIC Size: the Quick/Early Method,” Gabriele De Vito and Filomena Ferrucci, 2014 40
th
Euromicro Conference on Software Engineering and Advanced Applications, pp. 69-76.
711. “Software Development Cost Estimation Approaches – A Survey,” Barry Boehm, Chris Abts, and Sunita
Chulani, Ph. D. Qualifying program, Computer Science Department, University of Southern California,
1998.
712. “Software Estimation, Enterprise-Wide,” Vitalie Temmenco, IBM Developer Works, June 15, 2007.
713. “Practical Applications of Statistical Process Control,” Edward Weller, IEEE Software, May/June, 2000.
714. “Modern Tools to Support DoD Software Intensive System of Systems Cost Estimation,” Jo Ann Lane and
Barry Boehm, A DACS State of the Art Report, DACS Report Number 347336, 31 August 2007.
715. “CLB023 Software Cost Estimating,” Defense Acquisition University
716. “A Replicated Study on Correlating Agile Team Velocity in Function and Story Points,” Hennie Huijgens, 5
th
International Workshop on Emerging Trends in Software Metrics (WeTSOM 2014).
717. “Function Points, Use Case Points, Story Points: Observations From a Case Study,” Joe Schofield, Alan
Arementrout, and Regina Trujillo, CrossTalk: The Journal of Defense Software Engineering, May/June
2003.
718. “Phase wise Effort Estimation for Software Maintenance: An Extended SMEEM Model,” Jitender
Choudhari and Ugrasen Suman, CUBE’12, September 3–5, 2012.
719. “Estimate and Measure Agile Projects with Function Points,” Radenko Corovic.
720. “Hybridization of Class Responsibility Collaborators Model (HCRCM) with Function Point to enhance
Project Estimation Cost in Agile Software Development,” Faki Agebee Silas, Musa Yusuf, Anah Hassan Bijik,
Circulation in Computer Science, Vol. 2, No. 6, pp. 20-24, July 2017
5. Estimating Software Intensive System of Systems
Integration of Agile and Earned Value Management 34
721. “Counting Function Points for Agile / Iterative Software Development,” By Carol Dekkers, IFPUG,
http://www.ifpug.org/Articles/Dekkers-CountingAgileProjects.pdf
722. “An Empirical Study on Adjustment Factors to Estimate Maintenance Cost of Applications Developed
Using Components,” Byoung-Chol Lee and Sung Yul Rhew, Lecture Notes on Software Engineering, Vol. 2,
No. 1, February 2014.
723. “On XML Based Automated Function Point Analysis: An Effective Method to Assess Developer Productivity
Jeffrey S. Lent and Yanzhen Qu, Lecture Notes on Software Engineering, Vol. 3, No. 4, November 2015.
724. “Future Challenges for Software Data Collection and Analysis,” Barry Boehm, PROMISE Keynote, 2009
725. “How to Fix Agile teams that are Notoriously Bad at Hitting Release Dates,” Dr. Sven Jungmann and Alex
Loijos, Crunch Network, Jan 3, 2017.
726. “Estimation Bias and Mitigation with Agile Estimation Guidance, 2017 Edition,” Dan Galorath, 2016
727. “Uncertainty Analysis and the Project Cost Estimating Capability,” Andy Price, Brian Alford, Blake Boswell,
and Matt Pitlyk, NASA Cost Symposium, August 2014.
728. “Special Topics in Software Estimation: Software Cost Estimating for Iterative/Incremental Development
Programs – Agile Cost estimating,” NASA CAS, July 2014.
729. “Commercial-Like Acquisitions: Practices and Costs,” Wilmer Alvarado , Daniel Barkmeyer and Erik
Burgess, Journal of Cost Analysis and Parametrics, 3:1, 41-58, 2010.
730. “Agile Estimation Using Functional Metrics,” Tom Cagley
731. “Function Points and Agile – Hand in Hand,” Amol Kumar Keote, Accenture ‒ India Delivery Centre, 2010.
732. “Guideline for Sizing Agile Projects with COSMIC,” Sylvie Trudel and Luigi Buglione, IWSM/MetriKon 2010.
733. “Story Points or Function Points or Both?” David Consulting Group, July 2015
734. “Ranking and Clustering Software Cost Estimation Models through a Multiple Comparisons Algorithm,”
Nikolaos Mittas and Lefteris Angelis, IEEE Transactions of Software Engineering, Vol. 39, No. 4, April 2013.
735. “Estimation of the new agile XP process model for medium-scale projects using industrial case studies,”
M. Rizwan Jameel Qureshi, International Journal of Machine Learning and Computing, Vol. 3, No. 5,
October 2013.
736. “A deep learning model for estimating story points,” Morakot Choetkiertikul, Hoa Khanh Dam, Truyen
Tran, Trang Pham, Aditya Ghose, and Tim Menzies, ICSE ‘17
737. “Selecting Software Estimating Techniques that Fit the Software Process,” Kal Toth, PNSQC, 2009
738. “User Story Software Estimation: A Simplification Of Software Estimation Model With Distributed Extreme
Programming Estimation Technique,” Ridi Ferdiana, Paulus Insap Santoso, Lukito Edi Nugroho, and Ahmad
Ashari, JUTI, Volume 9, Number 1, January 2011, pp- 41-48
739. “Estimating Agile Iterations by Extending Function Point Analysis,” A. Udayan Banerjee, B. Kanakalata
Narayanan, and C. Mahadevan P, 2012 World Congress in Computer Science, Computer Engineering and
Applied Computing, Las Vegas, Nevada, July 16-19, 2012
740. “Agile and Function Points: A Winning Combination,” Dan French, 2016 ICEAA Professional Development
& Training Workshop, Atlanta, GA 2016.
741. “Early Software Project Estimation the Six Sigma Way,” Thomas Michael Fehlmann and Eberhard Kranich,
in Agile Methods. Large-Scale Development, Refactoring, Testing, and Estimation, pp 193-208
742. “Model-Based Dynamic Cost Estimation and Tracking Method for Agile Software Development,” Sungjoo
Kang, Okjoo Choi, and Jongmoon Baik, 9
th
IEEE/ACIS International Conference on Computer and
Information Science, IEEE/ACIS ICIS 2010, 18-20 August 2010, Yamagata, Japan
743. “A method to effort estimation for XP projects in clients perspective,” E. Karunakaran and N. Sreenath,
International Journal of Applied Engineering Research 10(7):18529-18550 · January 2015
744. “Functional Size Measures and Effort Estimation in Agile Development: A Replicated Study,” Valentina
Lenarduzzi, Ilaria Lunesu, Martina Matta, and Davide Taibi, Foundation of Computer Science FCS, New
York, USA. Volume 3– No. 7, August 2012
745. “Effort estimation in agile software development,” Andreas Schmietendorf, Martin Kunz, Reiner Dumke,
Proceedings 5
th
Software Measurement European Forum, Milan 2008
746. “From Story Points to COSMIC Function Points in Agile Software Development – A Six Sigma perspective,”
Thomas Fehlmann and Luca Santillo, MetriKon 2010
6. Estimating Software Intensive System of Systems
Integration of Agile and Earned Value Management 35
747. “An approach for effort estimation in incremental software development using cosmic function points,”
Freddy Paz, Claudia Zapata, and Jose Antonio Pow-Sang, The 8
th
ACM/IEEE International Symposium on
Empirical Software Engineering and Measurement, September 2014.
748. “Using Function Points in Agile Projects,” Célio Santana, Fabiana Leoneo, Alexandre Vasconcelos, and
Cristine Gusmão, Lecture Notes in Business Information Processing, May 2011.
749. “Software Intensive Systems Data Quality and Estimation: Research in Support of Future Defense Cost
Analysis,” Technical Report SERC-2012-TR-032-1, Systems Engineering Research Center, Stevens Institute
of Technology, March 16, 2012.
750. “Performance Analysis of the Software Cost Estimation Methods: A Review,” Sweta Kumari and Shashank
Pushkar, International Journal of Advanced Research in Computer Science and Software Engineering,
Volume 3, Issue 7, July 2013
751. “Statistical Analysis of various models of Software Cost Estimation,” T. N. Sharma, Anil Bhardwaj, G. R.
Kherwa, International Journal of Engineering Research and Applications (IJERA), Vol. 2, Issue 3, May-Jun
2012, pp. 683-685
752. “Extreme Software Cost Estimating,” Randall W. Jensen, Crosstalk: The Journal of Defense Software
Engineering, January 2004.
753. “An Impact of Linear Regression Models for Improving the Software Quality with Estimated Cost,” Arun
Kumar Marandi and Danish Ali Khan, Eleventh International Multi-Conference on Information Processing-
2015 (IMCIP-2015), 2015.
754. “A Novel Algorithm for Software Development Cost Estimation Based on Fuzzy Rough Set,” Rui Wang, Pin
Peng, Ling Xu, Xiao-xin Huang, and Xiu-ling Qiao, Journal of Engineering Science and Technology Review, 9
(4) (2016) 217 – 223.
755. “Software cost estimating for CMMI Level 5 developers,” Corinne C. Wallshein and Andrew G. Loerch,
Journal of Systems and Software, Volume 105, July 2015, Pages 72-78
756. “Communication of software cost estimates,” Magne Jørgensen, EASE2014.
757. “Optimism bias within the project management context,” James Prater, Konstantinos Kirytopoulos and
Tony Ma, International Journal of Managing Projects in Business, April 2017.
758. “Classification and Prediction of Software Cost through Fuzzy Decision Trees,” Efi Papatheocharous and
Andreas S. Andreou, Lecture Notes in Business Information Processing, Enterprise Information Systems,
234-247, May 2009
759. “Experimental Study Using Functional Size Measurement in Building Estimation Models for Software
Project Size,” Nelly Condori-Fernandez, Maya Daneva, Luigi Buglione, and Olga Ormanjieva, Eighth ACIS
International Conference on Software Engineering Research, Management and Applications, 2010.
760. “Uncertainty in through-life costing-review and perspectives,” Yee Mey Goh, Linda Newnes, Anthony
Mileham, Charis McMahon, and Mohammad Saravi, IEEE Transactions on Engineering Management,
57(4), pp. 689-701
761. “Review of hardware cost estimation methods, models and tools applied to early phases of space mission
planning,” O. Trivailo, M. Sippel, Y. A. Şekercioğlu, Progress in Aerospace Sciences, 53, pp. 1-17, 2012
762. “Unbounding Bounded Rationality: Heuristics As The Logic Of Economic Discovery,” Anna Grandori and
Magdalena Cholakova, International Journal Of Organization Theory And Behavior, 16 (3), pp. 368-392,
Fall 2013.
763. “Complexity in Defence Projects How Did We Get Here?” Mary McKinlay, Concept Symposium 2010,
Ocarsborg, Norway.
764. “Keys of Success: Software Measurement, Software Estimating, Software Quality,” Caper Jones, Namcook
Analytics, LLC, 4 August 2015.
765. “A Cost Model for Early Cost Calculation of Agile Deliveries,” Eric van der Vliet, ICEAA Workshop 2017.
766. “Error Propagation in Software Measurement & Estimation,” Luca Santillo, 20
th
IMEKO TC4 International
Symposium and 18
th
International Workshop on ADC Modeling and Testing.
767. “From requirements to project effort estimates – work in progress (still?),” Charles Symons and Cigdem
Gencel, REFSQ Conference, Essen, Germany, April 2013.
7. Estimating Software Intensive System of Systems
Integration of Agile and Earned Value Management 36
768. “Efficient Indicators to Evaluate the Status of Software Development Effort Estimation inside the
Organizations,” Elham Khatibi and Roliana Ibrahim, International Journal of Managing Information
Technology (IJMIT), Vol. 4, No. 3, August 2012.
769. “Towards an Early Software Effort Estimation Based on Functional and Non-Functional Requirements,”
Mohamed Kassab, Maya Daneva, and Olga Ormandjieva, IWSM/Mensura 2009, LNCS 5891, pp. 182–196,
2009.
770. “Recommending Effort Estimation Methods for Software Project Management,” Bernhard Peischl, Mihai
Nica, Markus Zanker, and Wolfgang Schmid, Proceedings of the IEEE/WIC/ACM International Conference
on Web Intelligence and Intelligent Agent Technology, Vol. 3 (WPRRS Workshop), Milano, Italy, 2009, pp.
77-80.
771. “A comparative evaluation of effort estimation methods in the software life cycle,” Popović Jovan, Bojić
Dragan, Computer Science and Information Systems, Volume 9, Issue 1, pp. 455-484
772. “Understanding of Software Effort Estimation at the Early Software Development of the Life Cycle ‒ A
Literature Review,” Kardile Vilas Vasantrao, International Journal of Engineering Research and
Applications, Vol. 2, Issue 1, Jan-Feb 2012, pp. 848-852
773. “Inaccurate Conception: Some Thoughts on the Accuracy of Estimates, Phillip Armour, Communications of
the ACM, March 2008, Vol. 51, No. 3.
774. “A Survey on Software Cost Estimation in the Chinese Software Industry,” Da Yang, Qing Wang, Mingshu
Li, Ye Yang, Kai Ye, and Jing Du, ESEM’08, October 9-10, 2008, Kaiserslautern, Germany.
775. “Improving Software Development Tracking and Estimation Inside the Cone of Uncertainty,” Pongtip
Aroonvatanaporn, Thanida Hongsongkiat, and Barry Boehm, Technical Report USC-CSSE-2012-504, Center
for Systems and Software Engineering, University of Southern California, 2012.
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814. “Fixed price without fixed specification,” Magne Jørgensen, Simula Research Laboratory, 15 March 2016.
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815. “The Use of Precision of Software Development Effort Estimates to Communicate Uncertainty,” Magne
Jørgensen, Software Quality Days. The Future of Systems-and Software Development. Springer
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Simula Research Laboratory & Institute of Informatics, University of Oslo.
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Accuracy,” Magne Jørgensen and D. I. K. Sjøberg, Simula Research Laboratory, Norway.
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Intervals,” Magne Jørgensen, Karl Halvor Teigen, and Kjetil Moløkken-Østvold, Journal of Systems and
Software, February 2004
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the-Art Report,” August 2007
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838. “The Business of Software Estimation Is Not Evil: Reconciling agile approaches and project estimates,”
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Qureshi, I.J., Information Technology and Computer Science, 2012, 8, 43–50.
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Organizations,” Elham Khatibi and Roliana Ibrahim, International Journal of Managing Information
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Development Effort,” Magne Jørgensen & Stein Grimstad, Simula Research Laboratory.
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11. Estimating Software Intensive System of Systems
Integration of Agile and Earned Value Management 40
861. “Improving Subjective Estimates Using Paired Comparisons,” Eduardo Miranda, IEEE Software,
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Integration of Agile and Earned Value Management 41
886. “Improving Subjective Estimations Using Paired Comparisons,” Eduardo Miranda, IEEE Software
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Project Management Journal, Volume 39, Issue 3, Pages 75-85, September, 2008.
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Information and Software Technology, Volume 51, Issue 9, September 2009, Pages 1327–1337.
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International Journal of Applied Information Systems (IJAIS), Volume 3, Number 7, August 2012.
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Koolmanojwong, and Jo Ann Lane, Center for Systems and Software Engineering, University of Southern
California, Los Angeles, 2013.
894. “Cost Estimation in Agile Development Projects,” Siobhan Keaveney and Kieran Conboy, International
Conference on Information Systems Development (ISD2011) Prato, Italy.
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International Journal of Computer Science and Information Security, Vol. 7, No. 3, 2010.
896. “Replanning, Reprogramming, and Single Point Adjustments,”
July 2013, NAVY CEVM (Center for Earned Value Management).
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th
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Workshop on Social Software Engineering (SSE), At Bergamo (Italy), September 2015.
900. “Object-Oriented Software Cost Estimation Methodologies Compared,” D. Gregory Foley & Brenda K.
Wetzel, Society of Cost Estimating and Analysis – International Society of Parametric Analysts, 22
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estimation-habits
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Grimstad, IEEE Transactions on Software Engineering, Vol. 38, No. 3, May/June 2012.
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Unpredictable–Behavior_20140219.pdf
907. ‘Practical Guidelines for Expert-Judgement-Based Software Effort Estimation,” Magna Jørgensen, IEEE
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13. Estimating Software Intensive System of Systems
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909. “Using the COSMIC Method to Estimate Agile User Stories,” Jean-Marc Desharnais, Luigi Buglione, Bugra
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Jørgensen Journal of Systems and Software (2015).
913. “Estimating Software Development Effort based on Use Cases – Experiences from Industry,” Bente Anda ,
Hege Dreiem , Dag I. K. Sjøberg1, and Magne Jørgensen, Proceedings of the 4th International Conference
on The Unified Modeling Language, Modeling Languages, Concepts, and Tools, Pages 487-502
914. “A Neuro-Fuzzy Model with SEER-SEM for Software Effort Estimation,” Wei Lin Du, Danny Ho, Luiz
Fernando Capretz, 25th International Forum on COCOMO and Systems/Software Cost Modeling, Los
Angeles, CA, 2010.
915. “A Program Manager's Guide For Software Cost Estimating,” Andrew L. Dobbs, Naval Postgraduate School,
December 2002.
916. “An Engineering Context For Software Engineering,” Richard D. Riehle, September 2008, Naval
Postgraduate School.
917. “Application of Real Options theory to software engineering for strategic decision making in software
related capital investments,” Albert O. Olagbemiro, Monterey, California. Naval Postgraduate School,
2008.
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and Parametrics, Volume 4, 2011 - Issue 2
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tentative analysis of model applicability to an ongoing development program,” William B. Collins, Naval
Postgraduate School
921. “An examination of project management and control requirements and alternatives at FNOC,” Charlotte
Ruth Gross, Naval Postgraduate School.
922. “Software cost estimation through Bayesian inference of software size, In Kyoung Park, Naval
Postgraduate School.
923. “Using the agile development methodology and applying best practice project management processes,”
Gary R. King, Naval Postgraduate School.
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International Forum on Systems, Software and COCOMO Cost Modeling, Washington, 2006.
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Conference, 13-17 August 2012, Dallas Texas
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Boehm , Ye Yang, Qing Wang, and Mingshu Li, ICSP 2007, LNCS 4470, pp. 37–48, 2007
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Integration of Agile and Earned Value Management 43
931. “Combining Estimates with Planning Poker – An Empirical Study,” Kjetil Moløkken-Østvold and Nils
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Planning Poker Technique,” Taghi Javdani Gandomani , Koh Tieng Wei, and Abdulelah Khaled Binhamid,
International Journal of Software Engineering and Its Applications, Vol. 8, No. 11 (2014), pp. 173-182.
933. “Algorithmic Based and Non-Algorithmic Based Approaches to Estimate the Software Effort,” WanJiang
Han , TianBo Lu , XiaoYan Zhang , LiXin Jiang and Weijian Li, International Journal of Multimedia and
Ubiquitous Engineering, Vol. 10, No. 4 (2015), pp. 141-154.
934. “Reducing Estimation Uncertainty with Continuous Assessment: Tracking the 'Cone of Uncertainty’”
Pongtip Aroonvatanaporn, Chatchai Sinthop and Barry Boehm, Center for Systems and Software
Engineering University of Southern California, Los Angeles, CA 90089, ASE’10, September 20–24, 2010,
Antwerp, Belgium, 2010.
935. “Integrated Approach of Software Project Size Estimation,” Brajesh Kumar Singh, Akash Punhani, and A. K.
Misra, International Journal of Software Engineering and Its Applications, Vol. 10, No. 2 (2016), pp. 45-64.
936. “Investigating the Effect of Using Methodology on Development Effort in Software Projects,” Vahid B.
Khatibi, Dayang N. A. Jawawi, and Elham Khatibi, International Journal of Software Engineering and Its
Applications, Vol. 6, No. 2, April, 2012.
937. “Data-Driven Decision Making as a Tool to Improve Software Development Productivity,” Mary Erin
Brown, Walden University, 2013
938. “Applying Agile Practices to Space-Based Software Systems,” Arlene Minkiewicz, Software Technology
Conference, Long Beach, CA 31 March – 3 April, 2014
939. “Estimating the Effort Overhead in Global Software Development,” Ansgar Lamersdorf, Jurgen Munch,
Alicia Fernandez-del Viso Torre, Carlos Rebate Sanchez, and Dieter Rombach, 5
th
IEEE International
Conference on Global Software Engineering, 2010
940. “A Proposed Framework for Software Effort Estimation Using the Combinational Approach of Fuzzy Logic
and Neural Networks,” Pawandeep Kaur and Rupinder Singh, International Journal of Hybrid Information
Technology, Vol. 8, No. 10 (2015), pp. 73-80.
941. “Software Estimating Rules of Thumb,” Capers Jones, http://www.compaid.com/caiinternet/ezine/capers-
rules.pdf
942. “Why Are Estimates Always Wrong: Estimation Bias and Strategic Misestimation,” Daniel D. Galorath,
http://www.iceaaonline.com/ready/wp-content/uploads/2015/06/RI03-Paper-Galorath-Estimates-
Always-Wrong.pdf
943. “Using planning poker for combining expert estimates in software projects,” K. Moløkken-Østvold, N. C.
Haugen, and H. C. Benestad, Journal of Systems and Software, vol. 81, issue 12 (2008) pp. 2106–2117.
944. “Effort Distribution to Estimate Cost in Small to Medium Software Development Project with Use Case
Points,” Putu Linda Primandari and Sholiq, The Third Information Systems International Conference, 2015
945. “Estimation of IT-Projects Highlights of a Workshop,” Manfred Bundschuh, Metrics News, Vol. 4, Nr. 2,
December 1999, pp. 29 – 37, https://www.itmpi.org/Portals/10/PDF/bundschuh-est.pdf
946. “Curbing Optimism Bias and Strategic Misrepresentation in Planning: Reference Class Forecasting in
Practice,” Bent Flyvbjerg, European Planning Studies, Vol. 16, No. 1, January 2008.
947. “The Use of Precision of Software Development Effort Estimates to Communicate Uncertainty,” Magne
Jørgensen, Software Quality Days. The Future of Systems-and Software Development, Springer
International Publishing, 2016.
948. “Numerical anchors and their strong effects on software development effort estimates,” Erik Løhrea, and
Magne Jørgensen, Journal of Systems and Software (2015).
949. “A Neuro-Fuzzy Model for Function Point Calibration,” Wei Xia, Danny Ho, and Luiz Fernando Capretz,
WSEAS, Transactions On Information Science & Applications, Issue 1, Volume 5, January 2008.
950. “Unit effects in project estimation: It matters whether you estimate in work-hours or workdays,” Magne
Jørgensen, Journal of Systems and Software (2015), https://www.simula.no/file/time-unit-effect-
woauthorinfpdf/download
951. “Fallacies and biases when adding effort estimates,” Magne Jørgensen, Proceedings at Euromicro/SEEA. :
IEEE, 2014.
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Integration of Agile and Earned Value Management 44
952. “How Does Project Size Affect Cost Estimation Error? Statistical Artifacts and Methodological Challenges,”
International Journal of Project Management, 30 (2012): 751-862,
https://www.simula.no/file/simulasimula742pdf/download
953. “Does the Use of Fibonacci Numbers in Planning Poker Affect Effort Estimates?” Ritesh Tamrakar and
Magne Jørgensen, 16
th
International Conference on Evaluation & Assessment in Software Engineering,
2012.
954. “Using inferred probabilities to measure the accuracy of imprecise forecasts,” Paul Lehner, Avra
Michelson, Leonard Adelman, and Anna Goodman, Judgment and Decision Making, Vol. 7, No. 6,
November 2012, pp. 728–740.
955. “Software Development Effort Estimation: Why it fails and how to improve it,” Magne Jørgensen, Simula
Research Laboratory & University of Oslo, https://www.simula.no/file/simulasimula1688pdf/download
956. “Contrasting Ideal and Realistic Conditions As a Means to Improve Judgment-Based Software
Development Effort Estimation,” Magne Jørgensen, Information and Software Technology, 53 (2011):
1382-1390.
957. Software Effort Estimation as Collaborative Planning Activity Kristin Børte,
https://www.simula.no/file/simulasimula1226pdf/download
958. “Human judgment in planning and estimation of software projects,”
https://www.simula.no/file/simulasimula886pdf/download
959. “Guideline for Sizing Agile Projects with COSMIC,” Sylvie Trudel and Luigi Buglione, IWSM/MetriKon, 2010.
960. “The COSMIC Functional Size Measurement Method, Version 3.0.1, Guideline for the use of COSMIC FSM
to Manage Agile Projects, VERSION 1.0,” September 2011.
961. “Using the COSMIC Method to Evaluate the Quality of the Documentation of Agile User Stories,” Jean-
Marc Desharnais, Buğra Kocatürk, and Alain Abran, Proceedings of the 12
th
International Conference on
Product Focused Software Development and Process Improvement, Pages 68-73
962. “An Empirical Study of Using Planning Poker for User Story Estimation,” Nils C. Haugen, Proceedings of
AGILE 2006 Conference (AGILE’06).
963. “A Framework for the Analysis of Software Cost Estimation Accuracy,” Stein Grimstad and Magne
Jørgensen, ISESE'06, September 21–22, 2006.
964. “An Empirical Investigation on Effort Estimation in Agile Global Software Development,” Ricardo Britto,
Emilia Mendes, and Jurgen Borstler, IEEE 10
th
International Conference on Global Software Engineering,
2015
965. “Software Effort Estimation: Unstructured Group Discussion as a Method to Reduce Individual Biases,”
Kjetil Moløkken and Magne Jørgensen, Incremental and Component-Based Software Development,
October 2003, University of Oslo.
966. “A Case Study on Agile Estimating and Planning using Scrum,” Viljan. Mahnic, Elektronika ir
Elektrotechnika (Electronics And Electrical Engineering), 2011. No. 5(111).
967. “Review on Traditional and Agile Cost Estimation Success Factor in Software Development Project,”
Zulkefli Mansor, Saadiah Yahya, Noor Habibah Hj Arshad, International Journal on New Computer
Architectures and Their Applications (IJNCAA) 1(3): 942-952.
968. “A Collective Study of PCA and Neural Network based on COCOMO for Software Cost Estimation,” Rina M.
Waghmode, L.V. Patil, and S.D Joshi, International Journal of Computer Applications (0975 – 8887) Volume
74– No. 16, July 2013.
969. “iUCP: Estimating Interactive-Software Project Size with Enhanced Use-Case Points,” Nuno Jardim Nunes,
Larry Constantine, and Rick Kazman, IEEE Software, Issue No. 04 - July/August (2011 vol. 28)
970. “Estimating Software Project Effort Using Analogies,” Martin Shepperd and Chris Schofield, IEEE
Transactions On Software Engineering, Vol. 23, No. 12, November 1997.
971. “Software Engineering Economics,” Barry W. Boehm, Software Information Systems Division, TRW
Defense Systems Group, Redondo Beach, CA 90278
972. “Adapting, Correcting, and Perfecting Software Estimates: A Maintenance Metaphor,” Tarek K. Abdel-
Hamid, , March 1993.
973. “Estimating software projects,” R. Agarwal, Manish Kumar t, Yogesh, S. Mallick, RM. Bharadwaj, D.
Anantwar, ACM Software Engineering Notes, Vol 26 No. 4 July 2001, Pg. 6.
16. Estimating Software Intensive System of Systems
Integration of Agile and Earned Value Management 45
974. “Cost estimation in agile development projects,” Siobhan Keaveney and Kieran Conboy, Proceedings of
the Fourteenth European Conference on Information Systems, ECIS 2006, Göteborg, Sweden, 2006.
975. “Managing Uncertainty in Agile Release Planning,” K. McDaid, D. Greer, F. Keenan, P. Prior, P. Taylor, G.
Coleman, Proceedings of the Eighteenth International Conference on Software Engineering & Knowledge
Engineering (SEKE'2006).
976. “Research Challenges of Agile Estimation,” Rashmi Popli, Dr. Naresh Chauhan, International Journal of IT &
Knowledge Management, Volume 7 • Number 1 • December 2013 pp. 108-111 (ISSN 0973-4414),
977. “Agile Software Development in Large Organizations, ” Mikael Lindvall, Dirk Muthig, Aldo Dagnino
Christina Wallin, Michael Stupperich, David Kiefer, John May, adn Tuomo Kähkönen, IEEE Computer,
December 2004.
978. "Adoption of Team Estimation in a Specialist Organizational Environment,“ Tor Erlend Fægri, Lecture
Notes in Business Information Processing, June 2010
979. “The Relationship between Customer Collaboration and Software Project Overruns,” Kjetil Moløkken-
Østvold and Kristian Marius Furulund, IEEE Agile Conference, 13-17 August, 2007.
980. “Improving Estimations in Agile Projects: Issues and Avenues,” Luigi Buglione, Alain Abran, Proceedings
Software Measurement European Forum (SMEF), 2007.
981. “Allowing for Task Uncertainties and Dependencies in Agile Release Planning,” Kevin Logue, Kevin McDaid,
and Des Geer, Proceedings Software Measurement European Forum (SMEF), 2007.
982. “Fundamental uncertainties in projects and the scope of project management,” Roger Atkinson , Lynn
Crawford, and Stephen Ward, International Journal of Project Management, 24 (2006) 687–698.
983. “Improving estimation accuracy by using Case Based Reasoning and a combined estimation approach,”
Srinivasa Gopal and Meenakshi D’Souza, Proceedings of ISEC '12, Feb. 22-25, 2012.
984. “Effort Estimation in Agile Software Development: A Systematic Literature Review,” Muhammad Usman ,
Emilia Mendes , Francila Weidt , and Ricardo Britto, Proceedings of the 10th International Conference on
Predictive Models in Software Engineering, 2014, Pages 82-91
985. “Incremental effort prediction models in Agile Development using Radial Basis Functions,” Raimund
Moser, Witold Pedrycz , and Giancarlo Succi, SEKE 2007
986. “Applying Combined Efforts of Resource Capability of Project Teams for Planning and Managing
Contingency Reserves for Software and Information Engineering Projects,” Peter H. Chang, GSTF Journal
on Computing (JoC), Vol. 2 No. 3, October 2012.
987. “Evidence-Based Software Engineering and Systematic Reviews,” Barbara Ann Kitchenham, David Budgen
and Pearl Brereton, November 5, 2015.
988. “Software Quality – Traditional vs. Agile: an Empirical Investigation,” Mohamad Kassab, JooYoung Lee,
and Manuel Mazzara, Giancarlo Succi, Rasul Tumyrkin, ArXiv, 2016
989. “The Signal and the Noise in Cost Estimating,” Christian B. Smart, Ph.D., 2016 International Training
Symposium, Bristol England, 2016.
990. “Effort estimation for Agile Software Development Projects,” Andreas Schmietendorf, Martin Kunz,
Reiner Dumke, Proceedings 5
th
Software Measurement European Forum, Milan 2008.
991. “The QUELCE Method: Using Change Drivers To Estimate Program Costs,” Sarah Sheard, April 2016,
Software Engineering Institute.
992. “Software Cost and Schedule Estimating: A Process Improvement Initiative,” Robert Park, Wolfhart
Goethert, J. Todd Webb, Special Report CMU/SEI-94-SR-3 May 1994.
993. “Organizational Considerations for the Estimating Process,” Bob Ferguson, Software Engineering Institute,
November, 2004.
994. “A Parametric Analysis of Project Management Performance to Enhance Software Development Process,”
N. R. Shashikumar, T. R. Gopalakrishnan Nair, Suma V, IEEE International Conference on Advanced
Research in Engineering and Technology (ICARET), 2013
995. “Checklists and Criteria for Evaluating the Cost and Schedule Estimating Capabilities of Software
Organizations,” Robert E. Park, CMU/SEI-95-SR-005
996. “A Manager's Checklist for Validating Software Cost and Schedule Estimates,” Robert E. Park, Special
Report CMU/SEI-95-SR-004, January 1995.
17. Estimating Software Intensive System of Systems
Integration of Agile and Earned Value Management 46
997. ACE Accurate Confident Estimating TSP Symposium November 4, 2014 Pittsburgh, PA, Team Software
Process (TSP) Symposium, 4 Nov 2014, SEI Carnegie Mellon University
998. “A Simulation and Evaluation of Earned Value Metrics to Forecast the Project Duration,” Mario
Vanhoucke and Stephan Vandevoorde, The Journal of the Operational Research Society, Vol. 58, No. 10
(Oct., 2007), pp. 1361-1374
999. “Avoid Software Project Horror Stories: Check the Reality Value of the Estimate First!”, Harold van
Herringen, ICEAA 2014
1000. COSMIC: Guideline for Sizing Business Software, Version 3, http://www.etsmtl.ca/Unites-de-
recherche/GELOG/accueil
1001. “Factors Affecting Duration And Effort Estimation Errors In Software Development Projects,” Ofer
Morgenshtern, Tzvi Raz, and Dov Dvir, Working Paper No 8/2005, Henry Crown Institute of Business
Research, Israel. http://recanati-bs.tau.ac.il/Eng/?CategoryID=444&ArticleID=747
1002. “An Empirical Validation of Software Cost Estimation Models,” Chris F. Kemerer, Research Contributions,
Management of Computing, Communications of the ACM , May 1987, Volume 30, Number 5.
1003. “A Decision Support System To Choose Optimal Release Cycle Length In Incremental Software
Development Environments,” Avnish Chandra Suman, Saraswati Mishra, and Abhinav Anand,
International Journal of Software Engineering & Applications (IJSEA), Vol.7, No.5, September 2016.
1004. “Protecting Software Development Projects Against Underestimation,” Eduardo Miranda, Alain Abran,
École de technologie supérieure - Université du Québec, http://mse.isri.cmu.edu/software-
engineering/documents/faculty-
publications/miranda/mirandaprotectingprojectsagainstunderestimations.pdf
1005. “Improving Subjective Estimates Using Paired Comparisons,” Eduardo Miranda, IEEE Software,
January/February 2001.
1006. “Improving Estimations in Agile Projects: Issues and Avenues,” Luigi Buglione, Alain Abran, Software
Measurement European Forum – SMEF2007, Rome (Italy), May 8-11, 2007.
1007. “Estimation of Software Development Effort from Requirements Based Complexity,” Ashish Sharma ,
Dharmender Singh Kushwaha, 2nd International Conference on Computer, Communication, Control and
Information Technology (C3IT 2012), February 25 - 26, 2012
1008. “Estimating the Test Volume and Effort for Testing and Verification & Validation,” Alain Abran, Juan
Garbajosa, , Laila Cheikhi1, First Annual ESA Workshop on Spacecraft Data Systems and Software - SDSS
2005, ESTEC, Noordwijk, European Space Agency, Netherlands, 17-20 October 2005.
1009. “A General Empirical Solution to the Macro Software Sizing and Estimating Problem,” Lawrence H.
Putnam, IEEE Transactions On Software Engineering, VOL. SE-4, NO. 4, JULY 1978.
1010. “A Comparison of Software Cost, Duration, and Quality for Waterfall vs. Iterative and Incremental
Development: A Systematic Review,” Susan M. Mitchell and Carolyn B. Seaman, Third International
Symposium on Empirical Software Engineering and Measurement, 2009.
1011. Software Sizing and Estimating: MK II FPA, Charles R. Symons, John Wiley and Sons, 1991
1012. “A Review of Surveys on Software Effort Estimation,” Kjetil Molkken and Magne Jorgensen, Proceeding of
International Symposium on Empirical Software Engineering, ISESE '03.
1013. “Accurate Estimates Without Local Data?” Tim Menzies, Steve Williams, Oussama Elrawas, Daniel Baker,
Barry Boehm, Jairus Hihn, Karen Lum, and Ray Madachy, Software Process Improvement And Practice,
(2009).
1014. “An Assessment and Comparison of Common Software Cost Estimation Modeling Techniques,” Lionel C.
Briand, Khaled El Emam, Dagmar Surmann, Isabella Wieczorek, and Katrina D. Maxwell, Proceedings of
the 21st international conference on Software engineering, Pg. 313-322
1015. “The Probable Lowest-Cost Alternative According to Borda,” Neal D. Hulkower, Journal of Cost Analysis
and Parametrics, 3:2, 29-36
1016. “An Efficient Approach for Agile Web Based Project Estimation: AgileMOW,” Ratnesh Litoriya and Abhay
Kothari, Journal of Software Engineering and Applications, 2013, 6, 297-303.
1017. “Corad Agile Method for Agile Software Cost Estimation,” Govind Singh Rajput and Ratnesh Litoriya,
http://dx.doi.org/10.4236/oalib.1100579
18. Estimating Software Intensive System of Systems
Integration of Agile and Earned Value Management 47
1018. “A Baseline Model for Software Effort Estimation,” Peter A. Whigham, Caitlin A. Owen, and Stephen G.
MacDonell, ACM Transaction on Software Engineering Methodology, 24, 3, Article 20 (May 2015).
1019. “Core Estimating Concepts,” William Roetzheim, CrossTalk: The Journal of Defense Software Engineering,
January/February 2013.
1020. “A Practical Approach to Size Estimation of Embedded Software Components,” Kenneth Lind and Rogardt
Heldal, IEEE Transactions On Software Engineering, Vol. 38, No. 5, September/October 2012.
1021. “A Probabilistic Model for Predicting Software Development Effort,” Parag C. Pendharkar, Girish H.
Subramanian, and James A. Rodger, IEEE Transactions On Software Engineering, Vol. 31, No. 7, July 2005.
1022. “A Pattern Language for Estimating,” Dmitry Nikelshpur, PLoP '11 Proceedings of the 18th Conference on
Pattern Languages of Programs, Article No. 17.
1023. “Do Estimators Learn? On the Effect of a Positively Skewed Distribution of Effort Data on Software
Portfolio Productivity,” Hennie Huijgens and Frank Vogelezang, 7
th
International Workshop on Emerging
Trends in Software Metrics, 2016.
1024. “The Inaccurate Conception: Some thoughts on the accuracy of estimates,” Phillip G. Armour,
Communications Of The ACM, March 2008/Vol. 51, No. 3
1025. “Understanding Software Project Estimates,” Katherine Baxter, Cross Talk: The Journal of Defense
Software Engineering, March/April 2009,
1026. “Validation Methods for Calibrating Software Effort Models,” Tim Menzies, Dan Port, Zhihao Chen, and
Jairus Hihn, May 15–21, 2005, http://menzies.us/pdf/04coconut.pdf
1027. “Requirements Instability in Cost Estimation,” Abiha Batool, Sabika Batool, and Mohammad Ayub Latif,
https://www.academia.edu/4493828/Requirements_Instability_in_Cost_Estimation
1028. “Negative Results for Software Effort Estimation Tim Menzies, Ye Yang, George Mathew, Barry Boehm,
Jairus Hihn, EMSE 2016.
1029. “Creating Requirements-Based Estimates Before Requirements Are Complete,” Carol A. Dekkers, Cross
Talk: The Journal of Defense Software Engineering, April 2005.
1030. “Rational Cost Estimation of Dedicated Software Systems,” Beata Czarnacka-Chrobot, Journal of Software
Engineering and Applications, 2012, 5, 262-269.
1031. “Summarization of Software Cost Estimation,” Xiaotie Qina and Miao Fang, Advances in Control
Engineering and Information Science, 2011
1032. “Software Project Development Cost Estimation,” Barbara Kitchenham, The Journal of Systems and
Software Volume 5, Issue 4, 267-278, November 1985.
1033. “Cost Estimation in Agile Software Development Projects,” Michael Lang, Kieran Conboy and Siobhán
Keaveney, International Conference on Information Systems Development (ISD2011) Prato, Italy.
1034. “Project Estimating and Scheduling,” Terry Boult, University of Colorado, Colorado Springs, CS 330
Software Engineering.
1035. “Practical Guidelines for Expert-Judgment-Based Software Effort Estimation,” Magne Jørgensen, IEEE
Software, May/June 2005.
1036. “Predicting Software Projects Cost Estimation Based on Mining Data,” Hassan Najadat, Izzat Alsmadi, and
Yazan Shboul, ISRN Software Engineering, Volume 2012, Article ID 823437.
1037. “Models for Improving Software System Size Estimates during Development,” William W. Agresti, William
M. Evanco, William M. Thomas, Journal of Software Engineering & Applications, 2010, 3: 1-10.
1038. “Requirements Engineering for Agile Methods,” Alberto Sillitti and Giancarlo Succi, in Engineering and
Managing Software Requirements, pp. 306-326, Springer, 2005.
1039. “A Method for Improving Developers’ Software Size Estimates,” Lawrence H. Putnam, Douglas T. Putnam,
and Donald M. Beckett, Cross Talk: The Journal of Defense Software Engineering, April 2005.
1040. “PERT, CPM, and Agile Project Management,” Robert C. Martin 5 October 2003,
http://www.codejournal.com/public/cj/previews/PERTCPMAGILE.pdf
1041. “Reliability and accuracy of the estimation process Wideband Delphi vs. Wisdom of Crowds,” Marek
Grzegorz Stochel, 35th IEEE Annual Computer Software and Applications Conference, 2011
1042. “Predicting development effort from user stories,” P. Abrahamsson, I. Fronza, R. Moser, J. Vlasenko, and
W. Pedrycz, International Symposium on Empirical Software Engineering and Measurement, 2011
19. Estimating Software Intensive System of Systems
Integration of Agile and Earned Value Management 48
1043. “Effort prediction in iterative software development processes - incremental versus global prediction
models,” Pekka Abrahamsson, Raimund Moser, Witold Pedrycz, Alberto Sillitti, Giancarlo Succi, First
International Symposium on Empirical Software Engineering and Measurement, 2007.
1044. “Planning Poker or How to avoid analysis paralysis while release planning,” James Grenning,
https://wingman-sw.com/papers/PlanningPoker-v1.1.pdf
1045. “Agile Estimation using CAEA: A Comparative Study of Agile Projects,” Shilpa Bhalerao , Maya Ingle, 2009
International Conference on Computer Engineering and Applications IPCSIT, Vol.2 (2011).
1046. “A Bayesian approach to improve estimate at completion in earned value management,” Franco Caron,
Fabrizio Ruggeri, and Alessandro Merli, Project Management Institute Journal, Vol. 44, No. 1, pp. 3-16.
2013.
1047. “An Empirical Approach for Estimation of the Software Development Effort,” Amit Kumar Jakhar and
Kumar Rajnish, International Journal of Multimedia and Ubiquitous Engineering, Vol. 10, No. 2 (2015), pp.
97-110.
1048. “Forecasting of Software Development Work Effort: Evidence on Expert Judgment and Formal Models,”
Magne Jørgensen, International Journal of Forecasting, 2007
1049. “Agile Release Planning: Dealing with Uncertainty in Development Time and Business Value,” Kevin Logue
and Kevin McDaid, 15th Annual IEEE International Conference and Workshop on the Engineering of
Computer Based Systems, March 31 – April 4, 2008.
1050. “Why Are Estimates Always Wrong: Estimation Bias and Strategic Misestimation,” Daniel D. Galorath,
AIAA SPACE 2015 Conference and Exposition Pasadena, California, 2015.
1051. “Estimation of Project Size Using User Stories,” Murad Ali, Zubair A Shaikh , Eaman Ali, International
Conference on Recent Advances in Computer Systems (RACS 2015).
1052. “A Survey of Agile Software Estimation Methods,” Hala Hamad Osman and Mohamed Elhafiz Musa,
International Journal of Computer Science and Telecommunications, Volume 7, Issue 3, March 2016.
1053. “Why Can’t People Estimate: Estimation Bias and Mitigation,” Dan Galorath, IEEE Software Technology
Conference, October 12-15, 2015
Hilton Hotel, Long Beach California
1054. “Cost-Effective Supervised Learning Models for Software Effort Estimation in Agile Environments,” Kayhan
Moharreri, Alhad Vinayak Sapre, Jayashree Ramanathan, and Rajiv Ramnath, 40th Annual IEEE Computer
Software and Applications Conference (COMPSAC), 2016
1055. “A Review of Surveys on Software Effort Estimation,” Kjetil Moløkken and Magne Jørgensen,
http://folk.uio.no/isu/INCO/Papers/Review_final8.pdf
1056. “Project Duration Forecasting Using Earned Value Method and Time Series,” Khandare Manish A., Vyas
Gayatri S., International Journal of Engineering and Innovative Technology (IJEIT) Volume 1, Issue 4, April
2012.
1057. “Integrating Risk Assessment and Actual Performance for Probabilistic Project Cost Forecasting: A Second
Moment Bayesian Model,” Byung-Cheol Kim, IEEE Transactions On Engineering Management, Vol. 62, No.
2, May 2015.
1058. “A study of project selection and feature weighting for analogy based software cost estimation,” Y.F. Li ,
M. Xie, and T.N. Goh, The Journal of Systems and Software 82 (2009) 241–252.
1059. “Complementing Measurements and Real Options Concepts to Support InterSprint Decision-Making in
Agile Projects,” Zornitza Racheva , Maya Daneva, Luigi Buglione, 34th Euromicro Conference Software
Engineering and Advanced Applications
1060. “Software Cost Estimation and Sizing Methods: Issues and Guidelines,” Shari Lawrence Pfleeger, Felicia
Wu, and Rosalind Lewis, RAND Corporation, Project Air Force, 2005.
1061. “Anchoring and Adjustment in Software Estimation,” Jorge Aranda and Steve Easterbrook, ESEC-FSE’05,
September 5–9, 2005, Lisbon, Portugal.
1062. “Cycle Time Analytics: Making Decision using lead Time and Cycle Time to avoid needing estimates for
every story,” Troy Magennis, LKCE 2013 ‒ Modern Management Methods
1063. “Probabilistic Forecasting Decision Making: When Do You Want it?” Larry Maccherone,
http://www.hanssamios.com/dokuwiki/_media/larry-maccherone-probabilistic-decision-making.pdf
20. Estimating Software Intensive System of Systems
Integration of Agile and Earned Value Management 49
1064. “Software Project Planning for Robustness and Completion Time in the Presence of Uncertainty using
Multi Objective Search Based Software Engineering,” Stefan Gueorguiev, Mark Harman, and Giuliano
Antoniol, GECCO’09, July 8–12, 2009, Montréal Québec, Canada.
1065. “Empirical Validation of Neural Network Models for Agile Software Effort Estimation based on Story
Points,” Aditi Panda, Shashank Mouli Satapathy, and Santanu Kumar Rath, 3
rd
International Conference on
Recent Trends in Computing, 2015 (ICRTC-2015).
1066. “When 90% Confidence Intervals are 50% Certain: On the Credibility of Credible Intervals,” Karl Halvor
Teigen and Magne Jørgensen, Applied Cognitive Psychology, 19: 455–475 (2005)
1067. “Scaling Agile Estimation Methods with a Parametric Cost Model,” Carl Friedrich Kreß, Oliver Hummel,
Mahmudul Huq, ICSEA 2014 : The Ninth International Conference on Software Engineering Advances, 2014
1068. “Expert Estimation and Historical Data: An Empirical Study,” Gabriela Robiolo, Silvana Santos, and Bibiana
Rossi, ICSEA 2013 : The Eighth International Conference on Software Engineering Advances
1069. “Agile Monitoring Using The Line Of Balance,” Eduardo Miranda, Institute for Software Research –
Carnegie-Mellon University and Pierre Bourque, École de technologie supérieure – Université du Québec
1070. “Managerial Decision Making Under Risk and Uncertainty,” Ari Riabacke, IAENG International Journal of
Computer Science, 32:4, IJCS_32_4_12
1071. “From Nobel Prize to Project Management: Getting Risks Right,” Bent Flyvbjerg, Aalborg University,
Denmark, Project Management Journal, vol. 37, no. 3, August 2006, pp. 5-15.
1072. “The Uncertainty Principle in Software Engineering,” Hadar Ziv and Debra Richardson, ICSE 97, 9
th
International Conference on Software Engineering, Boston MA, 17 ‒ 23 May, 1997
1073. “Analyzing Software Effort Estimation using k means Clustered Regression Approach,” Geeta Nagpal, Moin
Uddin, and Arvinder Kaur, ACM SIGSOFT Software Engineering Notes, January 2013 Volume 38 Number 1.
1074. “Assuring Software Cost Estimates: Is it an Oxymoron?,” Jairus Hihnl and Grant Tregre, Goddard Space
Flight Center, 2013 46
th
Hawaii International Conference on System Sciences.
1075. “How Does NASA Estimate Software Cost? Summary Findings and Recommendations,” Jairus Hihn, Lisa
VanderAar, Manuel Maldonado, Pedro Martinez, Grant Tregre, NASA Cost Symposium, OCE Software
Working Group, August 27-29, 2013.
1076. “Calibrating Software Cost Models Using Bayesian Analysis,” Sunita Chulani, Barry Boehm, Bert Steece,
USC-CSE 1998
1077. “Software Project and Quality Modelling Using Bayesian Networks,” Norman Fenton, Peter Hearty, Martin
Neil, and Łukasz Radliński, Elsevier, November 31, 2013.
1078. “Software Project Level Estimation Model Framework based on Bayesian Belief Networks,” Hao Wang, Fei
Peng, and Chao Zhang, 2006 Sixth International Conference on Quality Software (QSIC'06), 27-28 October,
2006.
1079. “Using Bayesian Belief Networks to Model Software Project Management Antipatterns,” Dimitrios Settas,
Stamatia Bibi, Panagiotis Sfetsos, Ioannis Stamelos, and Vassilis Gerogiannis, Software Engineering
Research, Management and Applications, ACIS International Conference on (2006), Seattle, Washington,
Aug. 9, 2006 to Aug. 11, 2006.
1080. “A Survey Of Bayesian Net Models For Software Development Effort Prediction,” Lukasz Radlinski,
International Journal Of Software Engineering And Computing, Vol. 2, No. 2, July-December 2010
1081. “Ten Unmyths of Project Estimation, Reconsidering some commonly accepted project management
practices,” Phillip Armour, Communications of the ACM, November 2002, Vol. 45, No. 11
1082. “Using Earned Value Data to Forecast the Duration and Cost of DoD Space Programs,” Capt. Shedrick
Bridgeforth Air Force Cost Analysis Agency (AFCAA).
1083. “Enterprise Agility—What Is It and What Fuels It?,” Rick Dove, in Utility Agility - What Is It and What Fuels
It? - Part 2, 10/24/2009.
1084. “Scrum Metrics for Hyperproductive Teams: How They Fly like Fighter Aircraft,” Scott Downey and Jeff
Sutherland, HICSS '13 Proceedings of the 2013 46th Hawaii International Conference on System Sciences,
Pages 4870-4878, IEEE Computer Society
1085. “Engaging Software Estimation Education using LEGOs: A Case Study,” Linda Laird and Ye Yang, IEEE/ACM
38th IEEE International Conference on Software Engineering Companion, 2016
21. Estimating Software Intensive System of Systems
Integration of Agile and Earned Value Management 50
1086. “Software Estimation – A Fuzzy Approach,” Nonika Bajaj, Alok Tyagi and Rakesh Agarwal, ACM SIGSOFT
Software Engineering Notes, Page 1 May 2006 Volume 31 Number 3.
1087. “Limits to Software Estimation,” J. P. Lewis, ACM SIGSOFT Software Engineering Notes Vol. 26 No. 4, July
2001, Page 54.
1088. “Recent Advances in Software Estimation Techniques,” Richard E. Fairley, ICSE '92: Proceedings of the
14th International Conference on Software Engineering, May 1992.
1089. “Software Estimation Using the SLIM Tool ,”Nikki Panlilio-Yap, Proceedings of the 1992 conference of the
Centre for Advanced Studies on Collaborative research - Volume 1, CASCON '92
1090. “An Approach for Software Cost Estimation,” Violeta Bozhikova and Mariana Stoeva, International
Conference on Computer Systems and Technologies - CompSysTech’10.
1091. “Estimating Software -Intensive Projects in the Absence of Historical Data,” Aldo Dagnino, ICSE '13:
Proceedings of the 2013 International Conference on Software Engineering, May 2013.
1092. “A Framework for Software Project Estimation Based on COSMIC, DSM and Rework Characterization,”
Sharareh Afsharian, Marco Giacomobono, and Paola Inverardi, BIPI’08, May 13, 2008.
1093. “Estimating software projects,” R. Agarwal, Manish Kumart, Yogesh, S. Mallick, RM. Bharadwaj, and D.
Anantwar, Software Engineering Notes vol 26 no 4 July 2001 Page 60.
1094. “Software Intensive Systems Cost and Schedule Estimation,” Technical Report SERC-2013-TR-032-2,
Stevens Institute of Technology, Systems Engineering Research Center, 31 June 2013.
1095. “Improved Size And Effort Estimation Models For Software Maintenance,” Vu Nguyen, University of
Southern California, December 2010.
1096. “Probabilistic Estimation of Software Project Duration,” Andy M. Connor, New Zealand Journal of Applied
Computing & Information Technology, 11(1), 11-22, 2007.
1097. “Application of Sizing Estimation Techniques for Business Critical Software Project Management,” Parvez
Mahmood Khan and M.M. Sufyan Beg, International Journal of Soft Computing And Software Engineering
(JSCSE), Vol. 3, No. 6, 2013
1098. “Double Whammy – How ICT Projects are Fooled by Randomness and Screwed by Political Intent,”
Alexander Budzier and Bent Flyvbjerg, Saïd Business School Working Papers, August 2011.
1099. “Software Cost Estimation Framework for Service-Oriented Architecture Systems using Divide-and-
Conquer Approach,” Zheng Li and Jacky Keung, Proceedings of the 5
th
International Symposium on Service-
Oriented System Engineering (SOSE 2010), pp. 47-54, Nanjing, China, June 4-5, 2010.
1100. “Investigating Effort Prediction Of Software Projects On The ISBSG Dataset,” Sanaa Elyassami and Ali Idri,
International Journal of Artificial Intelligence & Applications (IJAIA), Vol. 3, No. 2, March 2012.
1101. “Resource Estimation in Software Engineering,” Lionel C. Briand, Encyclopedia of Software Engineering,
John Wiley and Sons, 2001.
1102. “CECS 543/643 Advanced Software Engineering,” Dar-Biau Liu, California State University, Long Beach,
Spring 2012
1103. “Software Cost Estimation Review,” Alphonce Omondo Ongere, Helsinki Metropolia University of Applied
Sciences, 30 May 2013.
1104. “Software estimation process: a comparison of the estimation practice between Norway and Spain,” Paul
Salaberria, 1 December 2014, Universitetet I Bergenm.
1105. “Comparison of available Methods to Estimate Effort, Performance and Cost with the Proposed Method,”
M. Pauline, Dr. P. Aruna, Dr. B. Shadaksharappa, International Journal of Engineering Inventions, Volume
2, Issue 9 (May 2013), pp. 55-68
1106. “Applying Fuzzy ID3 Decision Tree for Software Effort Estimation,” Ali Idri and Sanaa Elyassam, IJCSI
International Journal of Computer Science Issues, Vol. 8, Issue 4, No 1, July 2011.
1107. “Software Effort Estimation with Ridge Regression and Evolutionary Attribute Selection,” Efi
Papatheocharous, Harris Papadopoulos and Andreas S. Andreou, 3
rd
Artificial Intelligence Techniques in
Software Engineering Workshop, 7 October, 2010.
1108. “A Comparison of Different Project Duration Forecasting Methods Using Earned Value Metrics,” Stephan
Vandevoorde and Mario Vanhoucke, International Journal of Project Management, 24 (2006), 289-302
1109. “Quantifying IT Forecast Quality,” J. L. Eveleens and C. Verhoef, Science of Computer Programming,
Volume 74, Issues 11–12, November 2009, Pages 934-988.
22. Estimating Software Intensive System of Systems
Integration of Agile and Earned Value Management 51
1110. “Predictive Modeling: Principles and Practices,” Rick Hefner, Dean Caccavo, Philip Paul, and Rasheed
Baqai, NDIA Systems Engineering Conference, pp. 20-23 October 2008.
1111. “Modeling, Simulation & Data Mining: Answering Tough Cost, Date& Staff Forecasts Questions,” Troy
Magennis and Larry Maccherone, Agile 2012, 13-17 August, 2013, Dallas Texas.
1112. “Enhance Accuracy In Software Cost And Schedule Estimation By Using 'Uncertainty Analysis And
Assessment’ In The System Modeling Process,” Kardile Vilas Vasantrao, International Journal of Research
& Innovation in Computer Engineering, Vol 1, Issue 1, (6-18), August 2011.
1113. “Estimating Perspectives, Richard D. Stutzke, 20
th
International COCOMO and Software Cost Modeling
Forum, Los Angeles 25-28 October 2005
1114. “Introduction to Systems Cost Uncertainty Analysis: An Engineering Systems Perspective,” Paul R. Garvey,
National Institute of Aerospace (NIA) Systems Analysis & Concepts Directorate NASA Langley Research
Center, 2 May 2006.
1115. “Cost and Schedule Uncertainty Analysis of Growth in Support of JCL,” Darren Elliot and Charles Hunt,
2014 NASA Cost Symposium, LaRC, 13 August 2014.
1116. “Measurement of Software Size: Contributions of COSMIC to Estimation Improvements,” Alain Abran,
Charles Symons, Christof Ebert, Frank Vogelezang, and Hassan Soubra, ICEAA International Training
Symposium, Bristol England, 2016.
1117. “What Does a Mature Cost Engineering Organization Look Like?” Dale Shermon, International Cost
Estimating and Analysis Association (ICEAA), 2016 18
th
to 20
th
October 2016.
1118. “A Hybrid Model for Estimating Software Project Effort from Use Case Points,” Mohammad Azzeh and Ali
Bou Nassif, Applied Soft Computing Journal, Elsevier
1119. “A Deep Learning Model for Estimating Story Points,” Morakot Choetkiertikul, Hoa Khanh Dam, Truyen
Tran, Trang Pham, Aditya Ghose, and Tim Menzies, ArXiv, 2016.
1120. “A Hybrid Intelligent Model for Software Cost Estimation,” Wei Lin Du, Luiz Fernando Capretz, Ali Bou
Nassif, Danny Ho, Journal of Computer Science, 9(11):1506-1513, 2013
1121. “An Empirical Analysis of Task Allocation in Scrum-based Agile Programming,” Jun Lin, Han Yu, Zhiqi Shen
1122. “Agile Planning & Metrics That Matter,” Sally Elatta, Agile Transformation for Government.
1123. “#NoEstimates, But #YesMeasurements: Why Shouldn’t agile teams waste their time and effort in
estimating,” Pekka Forselius, ISBSG IT Confidence Conference, 2016
1124. “Agile Benchmarks: What Can You Concluded?” Don Reifer, ISBSG IT Confidence Conference, 2016
1125. “Improve Estimation Maturity using Functional Size Measurement and Industry Data,” Drs. Harold van
Heeringen, ISBSG IT Confidence Conference, 2016
1126. “Why Can’t People Estimate: Estimation Bias and Mitigation,” Dan Galorath, ISBSG IT Confidence
Conference, 2015
1127. “Why Can’t People Estimate: Estimation Bias and Strategic Mis-Estimation,” Daniel D. Galorath, ISBSG IT
Confidence Conference, 2014
1128. “estimation Bias and Mitigation with Agile Estimation Guidance 2017 Edition,” Dan Galorath, Galorath
Corporation.
1129. “Estimation ‒ Next Level,” Ton Dekkers, ISBSG IT Confidence Conference, 2013.
1130. “Are We Really That Bad? A Look At Software Estimation Accuracy,” Peter R. Hill, ISBSG IT Confidence
Conference, 2013.
1131. “The Evaluation of Well-known Effort Estimation Models based on Predictive Accuracy Indicators,” Khalid
Khan, School of Computing Blekinge Institute of Technology, Sweden
1132. “A Knowledge and Analytics-Based Framework and Model for Forecasting Program Schedule
Performance, Kevin T. Knudsen and Mark Blackburn, Complex Adaptive Systems, Los Angeles, CA , 2016
1133. “Managing Project Uncertainty: From Variation to Chaos,” Arnound de Meyer, Christoph Loch, and
Michael Pich, IEEE Engineering Management Review 43(3):91 - 91 · December 2002
1134. “Project Uncertainty and Management Style,” C. H. Loch, M. T. Pich, and A. De Meyer,
2000/31/TM/CIMSO 10
1135. “Effects of Feature Complexity of Software Estimates ‒ An Exploratory Study,” Ana Magazinius and
Richard Berntsson Svensson, 40
th
Euromicro Conference on Software Engineering and Advanced
Applications, 2014