Software Metrics for Identifying Software Size in Software Development ProjectsVishvi Vidanapathirana
This paper defines the best software metrics that can be used to define the size of the software in the current industry of information technology (IT)
ANALYSIS OF SOFTWARE QUALITY USING SOFTWARE METRICSijcsa
Software metrics have a direct link with measurement in software engineering. Correct measurement is the prior condition in any engineering fields, and software engineering is not an exception, as the size and complexity of software increases, manual inspection of software becomes a harder task. Most Software Engineers worry about the quality of software, how to measure and enhance its quality. The overall objective of this study was to asses and analysis’s software metrics used to measure the software product and process.
In this Study, the researcher used a collection of literatures from various electronic databases, available since 2008 to understand and know the software metrics. Finally, in this study, the researcher has been identified software quality is a means of measuring how software is designed and how well the software conforms to that design. Some of the variables that we are looking for software quality are Correctness, Product quality, Scalability, Completeness and Absence of bugs, However the quality standard that was used from one organization is different from others for this reason it is better to apply the software metrics to measure the quality of software and the current most common software metrics tools to reduce the subjectivity of faults during the assessment of software quality. The central contribution of this study is an overview about software metrics that can illustrate us the development in this area, and a critical analysis about the main metrics founded on the various literatures.
A software system continues to grow in size and complexity, it becomes increasing difficult to
understand and manage. Software metrics are units of software measurement. As improvement in coding tools
allow software developer to produce larger amount of software to meet ever expanding requirements. A method
to measure software product, process and project must be used. In this article, we first introduce the software
metrics including the definition of metrics and the history of this field. We aim at a comprehensive survey of the
metrics available for measuring attributes related to software entities. Some classical metrics such as Lines of
codes LOC, Halstead complexity metric (HCM), Function Point analysis and others are discussed and
analyzed. Then we bring up the complexity metrics methods, such as McCabe complexity metrics and object
oriented metrics(C&K method), with real world examples. The comparison and relationship of these metrics are
also presented.
Software quality assurance (sqa) Parte II- Métricas del Software y Modelos d...Renato Gonzalez
• Complejidad del software y sus métricas
• Métricas cognitivas
• Métricas Funcionales
• Métricas de Proceso del Software
• Métricas de Complejidad Técnica del Producto
• Métricas de Calidad del software
• Atributos y Modelos de Calidad del Software
The Impact of Software Complexity on Cost and Quality - A Comparative Analysi...ijseajournal
Early prediction of software quality is important for better software planning and controlling. In early
development phases, design complexity metrics are considered as useful indicators of software testing
effort and some quality attributes. Although many studies investigate the relationship between design
complexity and cost and quality, it is unclear what we have learned beyond the scope of individual studies.
This paper presented a systematic review on the influence of software complexity metrics on quality
attributes. We aggregated Spearman correlation coefficients from 59 different data sets from 57 primary
studies by a tailored meta-analysis approach. We found that fault proneness and maintainability are most
frequently investigated attributes. Chidamber & Kemerer metric suite is most frequently used but not all of
them are good quality attribute indicators. Moreover, the impact of these metrics is not different in
proprietary and open source projects. The result provides some implications for building quality model
across project type.
A User Story Quality Measurement Model for Reducing Agile Software Developmen...ijseajournal
In Mobile communications age, the IT environment and IT technology update rapidly. The requirements
change is the software project must face challenge. Able to overcome the impact of requirements change,
software development risks can be effectively reduced. Agile software development uses the Iterative and
Incremental Development (IID) process and focuses on the workable software and client communication.
Agile software development is a very suitable development method for handling the requirements change in
software development process. In agile development, user stories are the important documents for the
client communication and criteria of acceptance test. However, the agile development doesn’t pay attention
to the formal requirements analysis and artifacts tracability to cause the potential risks of software change
management. In this paper, analyzing and collecting the critical quality factors of user stories, and
proposes the User Story Quality Measurement (USQM) model. Applied USQM model, the requirements
quality of agile development can be enhanced and risks of requirement changes can be reduced.
Software Metrics for Identifying Software Size in Software Development ProjectsVishvi Vidanapathirana
This paper defines the best software metrics that can be used to define the size of the software in the current industry of information technology (IT)
ANALYSIS OF SOFTWARE QUALITY USING SOFTWARE METRICSijcsa
Software metrics have a direct link with measurement in software engineering. Correct measurement is the prior condition in any engineering fields, and software engineering is not an exception, as the size and complexity of software increases, manual inspection of software becomes a harder task. Most Software Engineers worry about the quality of software, how to measure and enhance its quality. The overall objective of this study was to asses and analysis’s software metrics used to measure the software product and process.
In this Study, the researcher used a collection of literatures from various electronic databases, available since 2008 to understand and know the software metrics. Finally, in this study, the researcher has been identified software quality is a means of measuring how software is designed and how well the software conforms to that design. Some of the variables that we are looking for software quality are Correctness, Product quality, Scalability, Completeness and Absence of bugs, However the quality standard that was used from one organization is different from others for this reason it is better to apply the software metrics to measure the quality of software and the current most common software metrics tools to reduce the subjectivity of faults during the assessment of software quality. The central contribution of this study is an overview about software metrics that can illustrate us the development in this area, and a critical analysis about the main metrics founded on the various literatures.
A software system continues to grow in size and complexity, it becomes increasing difficult to
understand and manage. Software metrics are units of software measurement. As improvement in coding tools
allow software developer to produce larger amount of software to meet ever expanding requirements. A method
to measure software product, process and project must be used. In this article, we first introduce the software
metrics including the definition of metrics and the history of this field. We aim at a comprehensive survey of the
metrics available for measuring attributes related to software entities. Some classical metrics such as Lines of
codes LOC, Halstead complexity metric (HCM), Function Point analysis and others are discussed and
analyzed. Then we bring up the complexity metrics methods, such as McCabe complexity metrics and object
oriented metrics(C&K method), with real world examples. The comparison and relationship of these metrics are
also presented.
Software quality assurance (sqa) Parte II- Métricas del Software y Modelos d...Renato Gonzalez
• Complejidad del software y sus métricas
• Métricas cognitivas
• Métricas Funcionales
• Métricas de Proceso del Software
• Métricas de Complejidad Técnica del Producto
• Métricas de Calidad del software
• Atributos y Modelos de Calidad del Software
The Impact of Software Complexity on Cost and Quality - A Comparative Analysi...ijseajournal
Early prediction of software quality is important for better software planning and controlling. In early
development phases, design complexity metrics are considered as useful indicators of software testing
effort and some quality attributes. Although many studies investigate the relationship between design
complexity and cost and quality, it is unclear what we have learned beyond the scope of individual studies.
This paper presented a systematic review on the influence of software complexity metrics on quality
attributes. We aggregated Spearman correlation coefficients from 59 different data sets from 57 primary
studies by a tailored meta-analysis approach. We found that fault proneness and maintainability are most
frequently investigated attributes. Chidamber & Kemerer metric suite is most frequently used but not all of
them are good quality attribute indicators. Moreover, the impact of these metrics is not different in
proprietary and open source projects. The result provides some implications for building quality model
across project type.
A User Story Quality Measurement Model for Reducing Agile Software Developmen...ijseajournal
In Mobile communications age, the IT environment and IT technology update rapidly. The requirements
change is the software project must face challenge. Able to overcome the impact of requirements change,
software development risks can be effectively reduced. Agile software development uses the Iterative and
Incremental Development (IID) process and focuses on the workable software and client communication.
Agile software development is a very suitable development method for handling the requirements change in
software development process. In agile development, user stories are the important documents for the
client communication and criteria of acceptance test. However, the agile development doesn’t pay attention
to the formal requirements analysis and artifacts tracability to cause the potential risks of software change
management. In this paper, analyzing and collecting the critical quality factors of user stories, and
proposes the User Story Quality Measurement (USQM) model. Applied USQM model, the requirements
quality of agile development can be enhanced and risks of requirement changes can be reduced.
Contributors to Reduce Maintainability Cost at the Software Implementation PhaseWaqas Tariq
Software maintenance is important and difficult to measure. The cost of maintenance is the most ever during the phases of software development. One of the most critical processes in software development is the reduction of software maintainability cost based on the quality of source code during design step, however, a lack of quality models and measures can help asses the quality attributes of software maintainability process. Software maintainability suffers from a number of challenges such as lack source code understanding, quality of software code, and adherence to programming standards in maintenance. This work describes model based-factors to assess the software maintenance, explains the steps followed to obtain and validate them. Such a method can be used to eliminate the software maintenance cost. The research results will enhance the quality of the source code. It will increase software understandability, eliminate maintenance time, cost, and give confidence for software reusability.
ITERATIVE AND INCREMENTAL DEVELOPMENT ANALYSIS STUDY OF VOCATIONAL CAREER INF...ijseajournal
Software development process presents various types of models with their corresponding phases required to be accordingly followed in delivery of quality products and projects. Despite the various expertise and skills of systems analysts, designers, and programmers, systems failure is inevitable when a suitable development process model is not followed. This paper focuses on the Iterative and Incremental Development (IID)model and justified its role in the analysis and design software systems. The paper adopted the qualitative research approach that justified and harnessed the relevance of IID in the context of systems analysis and design using the Vocational
Career Information System (VCIS) as a case study. The paper viewed the IID as a change-driven software development process model. The results showed some system specification, functional specification of system and design specifications that can be used in implementing the VCIS using the IID model. Thus, the paper concluded that in systems analysis and design, it is imperative to consider a suitable development process that reflects the engineering mind-set, with heavy emphasis on good analysis and design for quality assurance.
A reliability estimation framework for OO design complexity perspective has been developed inthis paper. The proposed framework correlates the object oriented design constructs with complexity and also correlates complexity with reliability. No such framework has been available in the literature that estimates software reliability of OO design by taking complexity into consideration. The framework bridges the gap between object oriented design constructs, complexity and reliability. Framework measures and minimizes the complexity of software design at the early stage of software development life cycle leading to a reliable end product. Reliability and complexity estimation models have been proposed by following the proposed framework. Complexity estimation model has been developed which takes OO design constructs into consideration and proposed reliability estimation models take complexity in consideration for estimating reliability of OO design.
The presentation covers the overview of software metrics, its need, classification and important product metrics. It also explains GQM approach of metrics programme.
A Review on Software Fault Detection and Prevention Mechanism in Software Dev...iosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Evaluation of the software architecture styles from maintainability viewpointcsandit
In the process of software architecture design, different decisions are made that have systemwide
impact. An important decision of design stage is the selection of a suitable software
architecture style. Lack of investigation on the quantitative impact of architecture styles on
software quality attributes is the main problem in using such styles. So, the use of architecture
styles in designing is based on the intuition of software developers. The aim of this research is
to quantify the impacts of architecture styles on software maintainability. In this study,
architecture styles are evaluated based on coupling, complexity and cohesion metrics and
ranked by analytic hierarchy process from maintainability viewpoint. The main contribution of
this paper is quantification and ranking of software architecture styles from the perspective of
maintainability quality attribute at stage of architectural style selection.
One of the core quality assurance feature which combines fault prevention and fault detection, is often known as testability approach also. There are many assessment techniques and quantification method evolved for software testability prediction which actually identifies testability weakness or factors to further help reduce test effort. This paper examines all those measurement techniques that are being proposed for software testability assessment at various phases of object oriented software development life cycle. The aim is to find the best metrics suit for software quality improvisation through software testability support. The ultimate objective is to establish the ground work for finding ways reduce the testing effort by improvising software testability and its assessment using well planned guidelines for object-oriented software development with the help of suitable metrics.
Contributors to Reduce Maintainability Cost at the Software Implementation PhaseWaqas Tariq
Software maintenance is important and difficult to measure. The cost of maintenance is the most ever during the phases of software development. One of the most critical processes in software development is the reduction of software maintainability cost based on the quality of source code during design step, however, a lack of quality models and measures can help asses the quality attributes of software maintainability process. Software maintainability suffers from a number of challenges such as lack source code understanding, quality of software code, and adherence to programming standards in maintenance. This work describes model based-factors to assess the software maintenance, explains the steps followed to obtain and validate them. Such a method can be used to eliminate the software maintenance cost. The research results will enhance the quality of the source code. It will increase software understandability, eliminate maintenance time, cost, and give confidence for software reusability.
ITERATIVE AND INCREMENTAL DEVELOPMENT ANALYSIS STUDY OF VOCATIONAL CAREER INF...ijseajournal
Software development process presents various types of models with their corresponding phases required to be accordingly followed in delivery of quality products and projects. Despite the various expertise and skills of systems analysts, designers, and programmers, systems failure is inevitable when a suitable development process model is not followed. This paper focuses on the Iterative and Incremental Development (IID)model and justified its role in the analysis and design software systems. The paper adopted the qualitative research approach that justified and harnessed the relevance of IID in the context of systems analysis and design using the Vocational
Career Information System (VCIS) as a case study. The paper viewed the IID as a change-driven software development process model. The results showed some system specification, functional specification of system and design specifications that can be used in implementing the VCIS using the IID model. Thus, the paper concluded that in systems analysis and design, it is imperative to consider a suitable development process that reflects the engineering mind-set, with heavy emphasis on good analysis and design for quality assurance.
A reliability estimation framework for OO design complexity perspective has been developed inthis paper. The proposed framework correlates the object oriented design constructs with complexity and also correlates complexity with reliability. No such framework has been available in the literature that estimates software reliability of OO design by taking complexity into consideration. The framework bridges the gap between object oriented design constructs, complexity and reliability. Framework measures and minimizes the complexity of software design at the early stage of software development life cycle leading to a reliable end product. Reliability and complexity estimation models have been proposed by following the proposed framework. Complexity estimation model has been developed which takes OO design constructs into consideration and proposed reliability estimation models take complexity in consideration for estimating reliability of OO design.
The presentation covers the overview of software metrics, its need, classification and important product metrics. It also explains GQM approach of metrics programme.
A Review on Software Fault Detection and Prevention Mechanism in Software Dev...iosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Evaluation of the software architecture styles from maintainability viewpointcsandit
In the process of software architecture design, different decisions are made that have systemwide
impact. An important decision of design stage is the selection of a suitable software
architecture style. Lack of investigation on the quantitative impact of architecture styles on
software quality attributes is the main problem in using such styles. So, the use of architecture
styles in designing is based on the intuition of software developers. The aim of this research is
to quantify the impacts of architecture styles on software maintainability. In this study,
architecture styles are evaluated based on coupling, complexity and cohesion metrics and
ranked by analytic hierarchy process from maintainability viewpoint. The main contribution of
this paper is quantification and ranking of software architecture styles from the perspective of
maintainability quality attribute at stage of architectural style selection.
One of the core quality assurance feature which combines fault prevention and fault detection, is often known as testability approach also. There are many assessment techniques and quantification method evolved for software testability prediction which actually identifies testability weakness or factors to further help reduce test effort. This paper examines all those measurement techniques that are being proposed for software testability assessment at various phases of object oriented software development life cycle. The aim is to find the best metrics suit for software quality improvisation through software testability support. The ultimate objective is to establish the ground work for finding ways reduce the testing effort by improvising software testability and its assessment using well planned guidelines for object-oriented software development with the help of suitable metrics.
What to Test?! The Conversion Game ShowBrian Massey
If you know anything about your customers, you can profile them and make better decisions about what to test on your Web site to increase conversions to leads and sales. This "game show" showed that audience members could make good decisions even if they weren't Conversion Scientists. Presented at Pub Con South 2009 for the Conversion Testing And Optimization Panel with Bill Leake and Taylor Pratt.
ANALYSIS OF SOFTWARE QUALITY USING SOFTWARE METRICSijcsa
Software metrics have a direct link with measurement in software engineering. Correct measurement is the prior condition in any engineering fields, and software engineering is not an exception, as the size and complexity of software increases, manual inspection of software becomes a harder task. Most Software Engineers worry about the quality of software, how to measure and enhance its quality. The overall objective of this study was to asses and analysis’s software metrics used to measure the software product and process.
In this Study, the researcher used a collection of literatures from various electronic databases, available since 2008 to understand and know the software metrics. Finally, in this study, the researcher has been identified software quality is a means of measuring how software is designed and how well the software conforms to that design. Some of the variables that we are looking for software quality are Correctness, Product quality, Scalability, Completeness and Absence of bugs, However the quality standard that was used from one organization is different from others for this reason it is better to apply the software metrics to measure the quality of software and the current most common software metrics tools to reduce the subjectivity of faults during the assessment of software quality. The central contribution of this study is an overview about software metrics that can illustrate us the development in this area, and a critical analysis about the main metrics founded on the various literatures.
A study of various viewpoints and aspects software quality perspectiveeSAT Journals
Abstract The software quality is very important research of software engineering grown from the last two decades. The software engineering paradigm adopted by many organizations to develop the high quality software at affordable cost. The high quality software is considered as one of the key factor in the rapid growth of Global Software Development. The software metrics computes and evaluates the quality characteristics and used to take quantitative and qualitative decisions for risk assessment and reduction. The multiple stakeholders can view the software quality in multiple angles with various aspects. In this paper we present multiple views of the software quality with respect to various quality aspects. Key Words : Stakeholders, Functional aspect, Structural aspect, Process aspect, Metrics etc.
7.significance of software layered technology on size of projects (2)EditorJST
The objective of the software engineering is committed to build software projects within the budget, time and required quality. Software engineering is a layered paradigm comprised of process, methods, tools and quality focus as bedrock to develop the product. Software firms build software projects of varying sizes constrained on resources, time and functional requirement. Impact of software engineering layered technology may vary according to the size of the projects during their development. Quantitative evaluation of layer significance on size of the software project could be categorized as a complex task because it involves a collective decision on multiple criteria. Analytic Hierarchy Process (AHP) provides an effective quantitative approach for finding the significance of software layered technology on size of the projects. This paper presents the estimations through quantitative approach on real time data collected from several software firms. These findings help for a better project management with respect to the cost, time and resources during building a software project.
A novel defect detection method for software requirements inspections IJECEIAES
The requirements form the basis for all software products. Apparently, the requirements are imprecisely stated when scattered between development teams. Therefore, software applications released with some bugs, missing functionalities, or loosely implemented requirements. In literature, a limited number of related works have been developed as a tool for software requirements inspections. This paper presents a methodology to verify that the system design fulfilled all functional requirements. The proposed approach contains three phases: requirements collection, facts collection, and matching algorithm. The feedback results provided enable analysist and developer to make a decision about the initial application release while taking on consideration missing requirements or over-designed requirements.
An interactive approach to requirements prioritization using quality factorsijfcstjournal
As the prevalence of software increases, so does the complexity and the number of requirements assoc
iated
to the software project. This presents a dilemma for the developers to clearly identify and prioriti
ze the
most important requirements in order to del
iver the project in given amount of resources and time.
A
number of prioritization methods have been proposed which provide consistent results, but they are v
ery
difficult and complex to implement in practical scenarios as well as lack proper structure to
analyze the
requirements properly. In this study, the users can provide their requirements in two forms: text ba
sed
story form and use case form.
Moreover, the existing prioritization techniques have a very little or no
interaction with the users. So, in t
his paper an attempt has been made to make the prioritization process
user interactive by adding a second level of prioritization where after the developer has properly a
nalyzed
and ranked the requirements on the basis of quality attributes in the first le
vel, takes the opinion of distinct
user’s about the requirements priority sequence. The developer then calculates the disagreement valu
e
associated with each user sequence in order to find out the final priority sequence.
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.
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.
DESQA a Software Quality Assurance FrameworkIJERA Editor
In current software development lifecycles of heterogeneous environments, the pitfalls businesses have to face are that software defect tracking, measurements and quality assurance do not start early enough in the development process. In fact the cost of fixing a defect in a production environment is much higher than in the initial phases of the Software Development Life Cycle (SDLC) which is particularly true for Service Oriented Architecture (SOA). Thus the aim of this study is to develop a new framework for defect tracking and detection and quality estimation for early stages particularly for the design stage of the SDLC. Part of the objectives of this work is to conceptualize, borrow and customize from known frameworks, such as object-oriented programming to build a solid framework using automated rule based intelligent mechanisms to detect and classify defects in software design of SOA. The implementation part demonstrated how the framework can predict the quality level of the designed software. The results showed a good level of quality estimation can be achieved based on the number of design attributes, the number of quality attributes and the number of SOA Design Defects. Assessment shows that metrics provide guidelines to indicate the progress that a software system has made and the quality of design. Using these guidelines, we can develop more usable and maintainable software systems to fulfill the demand of efficient systems for software applications. Another valuable result coming from this study is that developers are trying to keep backwards compatibility when they introduce new functionality. Sometimes, in the same newly-introduced elements developers perform necessary breaking changes in future versions. In that way they give time to their clients to adapt their systems. This is a very valuable practice for the developers because they have more time to assess the quality of their software before releasing it. Other improvements in this research include investigation of other design attributes and SOA Design Defects which can be computed in extending the tests we performed.
Modern gadgets and machines such as medical equipments, mobile phones, cars and even military hardware run on software. The operational efficiency and accuracy of these machines are critical to life and the well being of modern civilization. When the software powering these machines fail it exposes life to danger and can cause the failure of businesses. In this paper, software quality measure is presented with the emphasis on improving standard and controlling damages that may result from badly developed application. The research shows various software quality standards and quality metrics and how they can be applied. The application of the metrics in measuring software quality in the research produced results which shows that the code metrics performance is better than the design metrics performance and points to a new way of improving quality by refactoring application code instead of developing new designs. This is believed to ensure reusability and reduced failure rate when software is developed
Electrically small antennas: The art of miniaturizationEditor IJARCET
We are living in the technological era, were we preferred to have the portable devices rather than unmovable devices. We are isolating our self rom the wires and we are becoming the habitual of wireless world what makes the device portable? I guess physical dimensions (mechanical) of that particular device, but along with this the electrical dimension is of the device is also of great importance. Reducing the physical dimension of the antenna would result in the small antenna but not electrically small antenna. We have different definition for the electrically small antenna but the one which is most appropriate is, where k is the wave number and is equal to and a is the radius of the imaginary sphere circumscribing the maximum dimension of the antenna. As the present day electronic devices progress to diminish in size, technocrats have become increasingly concentrated on electrically small antenna (ESA) designs to reduce the size of the antenna in the overall electronics system. Researchers in many fields, including RF and Microwave, biomedical technology and national intelligence, can benefit from electrically small antennas as long as the performance of the designed ESA meets the system requirement.
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
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.
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
Let's dive deeper into the world of ODC! Ricardo Alves (OutSystems) will join us to tell all about the new Data Fabric. After that, Sezen de Bruijn (OutSystems) will get into the details on how to best design a sturdy architecture within ODC.
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
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.
How world-class product teams are winning in the AI era by CEO and Founder, P...
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1. ISSN: 2278 – 1323
International Journal of Advanced Research in Computer Engineering & Technology (IJARCET)
Volume 2, No 5, May 2013
1811
www.ijarcet.org
Overview of Impact of Requirement Metrics in
Software Development Environment
1
Mohd.Haleem, 2
Prof (Dr) Mohd.Rizwan Beg, 3
Sheikh Fahad Ahmad
Abstract: Requirement engineering is the important area of
software development life cycle. Requirements engineering
play an important role in maintaining software quality.
Software quality depends on many factors like delivery on
time, within budget and fulfilling user’s needs. Software
requirements are the foundations through which quality can
be measured. Quality should be maintained from starting
phase of software development. Software quality during
requirement engineering process can be maintained through
requirement metrics. The objective of this paper is to
compare quality metrics involve in requirement engineering
process and analysing various requirement metrics, and
software quality metrics that are involve during
requirement process of a software development life cycle
and analysing impact of requirement metrics in software
development environment.
Keywords Software quality, Requirements metric Quality
Metrics, Requirements management.
I. INTRODUCTION
Software metric is a field of software engineering that is
associated with diverse measurements of computer
software and its developments. Software metrics [1] [2]
[3] is one of the important tools for analyzing the
software product in an effective way. In other words
software metrics are measures that enable software
developers and software analyst to gain insight into the
efficiency of the software process and projects that are
conducted using the process as framework. Software
metrics measures different aspects of software complexity
and therefore play an important role in analyzing and
improving software quality [3]. With the help of software
metric we are able to understand the software product in
an effective way. Software metric is a field of software
engineering that is associated with diverse measurements
of computer software and its development. According to
Tom DeMarco that “You cannot control what you cannot
measure”.Software metric [4] [5] are helpful in improving
the quality of software, planning the budget, its cost
estimation etc. with the help of software metric we are
able to understand the software product in an effective
way.
II. DEFINING: METRICS
Famous quote of a management consultant Peter Drucker:
“If you can‟t measure it, you can‟t manage it”, if a project
manager and team members are not able to precisely
measure what they are going to do then it would not be
possible for them to effectively manage and improve the
performance of a project. The success of a software
project is the primary goal. Metrics that help to measure
the success or failure of a project are very diverse and
these metric hardly have a good deal in commonalities
[6]. Metrics are also helpful to determine current status of
a project and evaluate its performance. Software metrics
can be classified into three categories: product metrics,
process metrics, and project metrics. Product metrics
describe the characteristics of the product such as size,
complexity, design features, performance, and quality
level. Process metrics can be used to improve software
development and maintenance. Project metrics describe
the project characteristics and execution.
III. QUALITY METRICS
Software quality metrics are the subset of software
metrics that focuses on the quality aspect of software.
Software quality metrics are associated with process and
product metrics than with project metrics. Software
quality metrics can be divided further into end-product
quality metrics and in-process quality metrics. The need
of software quality engineering is to determine the
relationships among in-process metrics, project
characteristics, and end-product quality in order to
improve quality in both process and product. Moreover,
we should view quality from the entire software life-cycle
perspective and, we should include metrics that measure
the quality level of the maintenance process as another
category of software quality metrics.
IV. SOFTWARE REQUIREMENT METRICS
Requirement engineering deals with elicitation, analysis,
communication and validation of the requirements. As
soon as errors are identified in initial stage, it is easy to
fix them as compared to later identification of errors. And
the cost of fixing these errors in initial stages is much
lower than fixing them in later stages of software
development. Metrics that can be collected during
requirements are the size of the requirements,
requirements traceability, requirements completeness and
requirements volatility [7].
1) Size Metrics: Size is an important metrics that are
used to measure requirements. LOC is common metrics
used to measure size of the software. Comment lines and
blank lines are not considered as code. A Non commented
line is considered as a code but it also include executable
commands, executable statements and data declarations.
Use Cases can also be considered as a size measurement
when they are used to describe requirements. For example
number of use cases, number of functions covered etc.
Use case metrics are categorized into 3 categories [8].
Size metrics: Size metrics includes number of atomic
actions in main flow. Size of use case can be determined
by counting number of actors weighted by their
complexity.
Environment metrics: Environment metrics are the
factors which has influence on complexity level of the use
cases. These are independent of the size of the use cases.
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International Journal of Advanced Research in Computer Engineering & Technology (IJARCET)
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Composite metrics: Composite metrics are derived
from size metrics and environment metrics.
2) Traceability Metrics: Traceability is the ability to
trace requirements in a specification to their origin from
higher level to lower level requirements in a set of
documented links. Traceability provides information
which helps in determining whether all relationship and
dependencies are addressed. This also makes
requirements leakage - existence of lower level
requirements with no valid origin from higher level
visible. It helps in preventing wrong interpretations of
other metrics. There exist 5 types of traceability metrics
[9].
Next level coverage metrics: This metric traces number
of requirements to next level up and next level down.
This metrics also trace number of requirements in
both directions.
Full depth and height coverage: This is similar to next
level coverage metrics and best suited for 3 or less level
specifications. It traces requirements to highest and
lowest level of specifications instead of only immediate
next level in both directions.
Inconsistent traceability metrics: This includes the
numbers of requirements in a specification that have at
least one inconsistent link in both upward and downward
directions.
Undefined traceability metrics: It include the numbers
of requirements in a specification that have no traceability
links upward and download. However, the highest link
will not have any upward link as it is derived from design
document and the lowest level link has no down link.
Linkage statistic metric: It measure the complexity
level of traceability data by counting the number of
higher or lower level requirements to which each
requirements specification is traced.
3) Completeness Metrics: This metrics are used to
assess whether a requirement is at wrong level of
hierarchy or too complex. Requirements are considered as
complete if all pages are numbered, all figures and tables
have captions and numbers, all references are present, all
sections and subsections are numbered. Requirements
Completeness Metrics are of 3 types [8].
Requirements Decomposition Metrics: This metric is
used to know whether the requirements specified have
been decomposed sufficiently to be used in next phase. A
complex function will have many levels of specification
and a simple function will have few levels.
Requirements Specification Development Metrics:
This metric is used to provide information about the work
completed and the work remaining for a given
specification.
Requirements Specification Metrics: This metric
provide information about the quantity of items for which
issue resolution is needed. This metric used „to be
determined‟, „to be supplied‟ tags used for completing the
requirements document.
4) Volatility metrics: The degree to which requirement
changes over a time period is called volatility of
requirements. This metric is used to assess reasons for
change of requirements over a period of time.
Requirements degree of change is also checked by this
metric. Both these factors are checked to know whether
the changes are consistent with current development
activities. It indicates changes such as addition, deletion
and modifications. It helps in tracing future requirements,
design and code volatility. Volatility can be high in the
initial phase of software development. It should be
reduced as the project progress so that further
development should not be affected. This metric is a good
indication of other metrics for example, if a specific
portion of software is tested without knowing the
volatility of the requirements than this testing would not
be wrathful.
V. REQUIREMENT QUALITY METRICS
Use cases and other traditional tools are used to collect
and frame the system architecture. However, to check the
quality of collected requirements, many requirement
quality metrics are discussed in the literature which
remains as open issues for further improvement [10].
1) Unambiguous: Requirement collection is a crucial
phase of SDLC since ambiguous and wrong requirements
may cause system failure. Since analysing a scenario in to
classes and objects is up to one‟s perception, ambiguity
may get induced among reviewers leading to an undesired
system.
Ridr
QUA=∑ ───
N
Where Ridr is number of requirements reviewed identical
by reviewers,
N is total number of requirements.
2) Correctness: A Use case Scenario explores numerous
functional requirements. However, the right interpretation
of user vocabulary will lead to correct set of valid
requirements. Vague requirements like „shall‟, „multi-
user‟, „user-friendly‟, are thoroughly analysed as an input
to design phase.
QC= Nv/(Nnv*N)
where
Nv is number of valid requirements,
Nnv is number of still not valid requirements and
N is Total number of requirements
3) Completeness: It reflects the depth/ breadth of
requirements in a scenario. Each requirement is unique
and need to serve the user as a complete package. Though
many requirement metrics are discussed and practiced, it
is hard to quantify the quality of requirements metric.
Organisational guidelines and best practices will
improvise the requirement collection to analysis process
including their traceability and volatility. However,
involving the stakeholders in requirement analysis would
reduce the confusion and frequent changes due to
uncertainty. Having a count on requirement changes other
than business change will give an insight on process
adapted.
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International Journal of Advanced Research in Computer Engineering & Technology (IJARCET)
Volume 2, No 5, May 2013
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VI. COMPARISION OF QUALITY METRICS
In this section we have tried to compare a set of quality
metrics in requirement engineering that are used to ensure
the quality of the requirement and hence improving
overall quality of the product and also analyse their
meaning in terms of requirement.
Table 1: Quality Metrics In Requirement Engineering
Quality Metrics Formula Purpose Conclusion
Unambiguous
Rid
Qua =----------
Rt
Where Rid is the no of identical requirements
Rt is the total no. of requirements.
To obtain the identical
requirements that has been
interpreted in a uniquely by
all reviewers.
0 or Close to 0 means
requirements are
Ambiguous.
1 or Close to 1 means
requirements are
Unambiguous
Completeness
Run
Qcm =----------
Rt
Where Run is the no of unique requirements,
Rt is the total no. of requirements.
To measure the total number
of functions currently
specified.
As closer to 1, then more
complete it becomes
Correct
Rip
Qcr =----------
Rt
Where Rip is the no of requirements having
same interpretation
Rt is the total no. of requirements.
To calculate the requirements
that have been correctly
validated
0 means incorrect
1 means correct
Consistent
Run - Rn
Qcn = ----------
Rn
where, Run is the number of unique functions
specified, Rn is the number of unique
functions that are non deterministic
To measure the percentage of
unique functions that are
deterministic assuming that
SRS can be defined as a
function that maps inputs and
states in to outputs.
0 means incosistent
1 means consistent
Uniqueness
Rdi
Qun = ----------
Rt
where, Rdi is the requirement with distinct
explanation,Rt is the total no. of requirements.
To obtain the no of unique
requirement that have been
explained by all reviews
As closer to 1, then more
unique it becomes
Understandable
Rus
Qun = ----------
Rt
where, Rus is the requirement understood by
the users, Rt is the total no. of requirements.
To measure the number of
requirements those are
understood by all reviewers.
0 means No requirement
understood.
1 means All
requirements understood.
Modifiable
Rmo
Qun = ----------
Rt
where, Rmo is the no of requirements
modified Rt is the total no. of requirements.
To count the no. of
requirements that are required
to changed or modified Not Defined
Traced
Rtr
Qtr = ----------
Rt
where, Rtr is the no of requirements traced
Rt is the total no. of requirements.
To measure level of tracing
the requirement and any
changes is difficult because
this activity spans throughout
the SDLC
Not Defined
Traced
Rtr
Qtr = ----------
Rt
where, Rtr is the no of requirements traced,Rt
is the total no. of requirements.
To measure level of tracing
the requirement and any
changes is difficult because
this activity spans throughout
the SDLC
Not Defined
Verifiable
Rt
Qvr = --------------
Rt + C+ T
Rt is the total number of requirements C is the
cost necessary to verify presence of
requirements
T is the time necessary to verify presence of
requirements
To measure the verifiability of
a requirement.
0 means Very poor.
1 means Very good.
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Volume 2, No 5, May 2013
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VII . IMPACT OF REQUIREMENT METRICS
Requirements are the foundation of the software
development. The success of software product, both
functionally and financially is directly related to the
quality of the requirements. Although the initial sets of
requirements are well documented, requirements changes
will occur during the software development lifecycle.
Thus constant changes (addition, deletion, modification)
in requirements during the development
lifecycle impact the cost, the schedule and the quality of
final product.
One way to understand the importance of requirements is
to look at the costs of correcting errors. As an example,
the typical relative costs of correcting an error at a
particular stage of software development is given in
figure 1.1 [11]. As an example, an error detected in the
requirements phase is 0.2
Phase Relative Cost
Requirements 0.1-0.2
Design 0.5
Coding 1
Unit Testing 2
Acceptance Testing 5
Maintenance 20
Figure 1.1: The relative costs of correcting errors made at
the requirements stage of a project.
times as costly to fix as an error detected in the coding
stage. An error detected in the maintenance phase is 20
times as costly to fix as an error detected in the coding
stage. Figure 1.1 gives the cost of detecting and repairing
an error in the coding phase relative to the cost of
detecting and repairing errors in other phases.
Figure 1.2 shows the cumulative effects of errors made at
the various stages. Other studies, such as the one reported
in Sallis et.al. [12] estimate that the percentage of errors
in released software due to specifically to requirements to
be in excess of 50% of all errors detected. Similar
evidence suggests that concentrating effort earlier in
software development processes can have a dramatic
effect
VII. CONCLUSION
Quality is an uncompromised factor in software
development process. Software deliverables are checked
for quality at every phase of development process to
ensure the quality of end product. Quality process is
organization-specific providing a basement for quality
product. Numerous metrics are used by various industries
to measure the quality of requirement phase to
implementation phase to uphold them in the market.
Requirements metrics discussed checks the degree of
change of requirements, volatility. Traceability, process
of linking high level to low level requirements.
Incompleteness in requirements documents is also
checked. Quality factors such as completeness,
correctness, understanding etc. are discussed and their
corresponding formulae are mentioned. These
requirements metrics help in identifying and rectifying
errors in requirements document. However, research in
this area is in continual progress to provide better metrics
for Product, People, Process to support the development
of software. Implementation of quality metrics during the
development process ensures production of high quality
software.
REFERENCES
[1] H F Li, W K Cheung “An Empirical Study of
Software Metrics” Software Engineering IEEE
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Static and Dynamic Metrics for Productivity and Time
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[5] S.R Chidamber and C.F. Kemerer, “A Metrics Suite
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[8]. Zohra Bellahsene, Dilip Patel, and Colette Rolland.
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International Journal of Advanced Research in Computer Engineering & Technology (IJARCET)
Volume 2, No 5, May 2013
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[9]. Rita J. Costello and Dar-Biau Liu. Metrics for
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[10] Mohammad Ubaidullah Bokhari and Shams Tabrez
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Author Profile
Mohd. Haleem
received B.Sc(Hons) degree from Aligarh Muslim
University in 2005 and MCA degree from HBTI, Kanpur
in 2009 and currently pursuing M.Tech degree in Computer
Science & Engg from Integral University, Lucknow. From 2011
onwards he is working as a Lecturer in the department of
Computer Application, Integral University, Lucknow,India.
Prof. (Dr.) Mohd Rizwan Beg
received B.Tech degree in Computer Science & Engg in
1995 and completed his M.Tech and PhD degree in
Software Engineering. Currently he is working as the
Dean of Computer Application Deptt and Head of
Computer Science & Engg Deptt, Integral University,
Lucknow. He is also the member of Editorial Boards,
Program and Technical Committees of many
International Journals as well as he is in the advisory
committees of many International Journals. Currently he
is supervising eight candidates in PhD Program and he
has around 78 International Publications.
Sheikh Fahad Ahmad
received his B.Tech degree in Computer Science & Engg. from
Integral University, Lucknow in 2008 and currently pursuing
M.Tech degree in Computer Science & Engg from Integral
University, Lucknow. From 2010 onwards he is working as a
Assistant Professor in the department of Computer Science &
Engg, Integral University, Lucknow, India.