This document discusses various software project estimation methods, including bottom-up, top-down, parametric, and analogy-based estimation. It describes the COCOMO model for estimating software development effort and provides details on its constants, development effort multipliers, and the stages of analogy-based estimation. Function point models like Mark II and COSMIC are also summarized as methods of measuring software size.
Effort estimation is the process of predicting the most realistic amount of effort (expressed in terms of person-hours or money) required to develop or maintain software based on incomplete, uncertain and noisy input.
Effort estimation is essential for many people and different departments in an organization.
Resource Allocation In Software Project ManagementSyed Hassan Ali
Resource Allocation In Software Project Management
what is Resource Allocation In Software Project Management
define Resource Allocation In Software Project Management
how to allocate resource in software project management
Effort estimation is the process of predicting the most realistic amount of effort (expressed in terms of person-hours or money) required to develop or maintain software based on incomplete, uncertain and noisy input.
Effort estimation is essential for many people and different departments in an organization.
Resource Allocation In Software Project ManagementSyed Hassan Ali
Resource Allocation In Software Project Management
what is Resource Allocation In Software Project Management
define Resource Allocation In Software Project Management
how to allocate resource in software project management
Software Project Management | An Overview of the Software Project ManagementAhsan Rahim
Management is the process of getting things done through others, it is the process of coordinating people & other resources to achieve the goals of the organization. A project is a set of related tasks that are coordinated to achieve a specific objective in a given time limit. A project is well-defined task, which is a collection of several operations done in order to achieve a goal. Software is the program & all associated documentation & configuration data which is needed to make these programs operate correctly.
A Software Project is the complete procedure of software development from requirement gathering to testing & maintenance, carried out according to the execution methodologies, in a specified period of time to achieve intended software product.
Software Project Management: Risk ManagementMinhas Kamal
Software Project Management: ResearchColab- Risk Management (Document-7)
Presented in 4th year of Bachelor of Science in Software Engineering (BSSE) course at Institute of Information Technology, University of Dhaka (IIT, DU).
Software Project Management | An Overview of the Software Project ManagementAhsan Rahim
Management is the process of getting things done through others, it is the process of coordinating people & other resources to achieve the goals of the organization. A project is a set of related tasks that are coordinated to achieve a specific objective in a given time limit. A project is well-defined task, which is a collection of several operations done in order to achieve a goal. Software is the program & all associated documentation & configuration data which is needed to make these programs operate correctly.
A Software Project is the complete procedure of software development from requirement gathering to testing & maintenance, carried out according to the execution methodologies, in a specified period of time to achieve intended software product.
Software Project Management: Risk ManagementMinhas Kamal
Software Project Management: ResearchColab- Risk Management (Document-7)
Presented in 4th year of Bachelor of Science in Software Engineering (BSSE) course at Institute of Information Technology, University of Dhaka (IIT, DU).
Developers are in constant search for greenfield projects because it allows them to be free of constraints. But over time, I started to appreciate the innovation found within working around constraints.
The goal of this presentation is use two experiences in my career which highlights constraints which begged for innovation. Only on my retrospective of these developments, one can appreciate the true novelty in constraints.
Grails has great performance characteristics but as with all full stack frameworks, attention must be paid to optimize performance. In this talk Lari will discuss common missteps that can easily be avoided and share tips and tricks which help profile and tune Grails applications.
Software_effort_estimation for Software engineering.pdfsnehan789
calculating software effort estimation for subjects like software engineering and software project management all according to your college preference on the subject
Building a Real-Time Security Application Using Log Data and Machine Learning...Sri Ambati
Building a Real-Time Security Application Using Log Data and Machine Learning- Karthik Aaravabhoomi
- Powered by the open source machine learning software H2O.ai. Contributors welcome at: https://github.com/h2oai
- To view videos on H2O open source machine learning software, go to: https://www.youtube.com/user/0xdata
App Modernization with .NET Core: How Travelers Insurance is Going Cloud-NativeVMware Tanzu
At Travelers Insurance, a decade old rating engine was still on the mainframe, making it difficult to scale, expensive to run, and reliant on a shrinking pool of skilled engineers. Rating engines are fundamental to the insurance quoting process, and at the very heart of the business. It was time to modernize and take advantage of the scalability, stability, automation and fast iteration cycles made possible by cloud-native architecture.
In this webinar, Viraj Naik of Travelers Insurance and Rohit Kelapure of Pivotal will take us on their journey from mainframe to microservices. Viraj and Rohit will describe how they built a distributed, event-driven rating engine with .NET Core on Pivotal Platform using Steeltoe, running on Linux stem cells. The new rating engine exceeded SLAs and reduced time to production to under 60 minutes.
You’ll learn the keys to a successful mainframe rewrite-based modernization, including:
● A pragmatic, domain-driven approach and phased delivery, including implementing a strangler pattern and anti-corruption layers.
● How to port business objects and business rules from mainframe to .NET.
● New innovations developed and delivered during the migration process.
Speakers:
Viraj Naik, Lead Solutions Architect at Travelers Insurance
Rohit Kelapure, Principal Solution Architect at Pivotal
Addressing Uncertainty How to Model and Solve Energy Optimization Problemsoptimizatiodirectdirect
During the past twenty years, IBM's CPLEX Optimization Studio has been used extensively to solve hard Energy Optimization Problems. CPLEX Optimization Studio's modeling capabilities, fast solvers and easy deployment features empower users to deploy energy applications quickly and reliably. Now, with a newly update superset offering know as Decision Optimization Center, the capabilities for this class of problems is greatly enhanced. Using the Unit Commitment Problem as a paradigm, we will demonstrate the advantages of using Decision Optimization Center, and CPLEX for modeling complex problems and application developments in the Energy Field.
Most importantly we will introduce new research developed for solving problems that address Uncertainty. We will showcase a new research approach that allows the user to test and deploy Stochastic, Robust or Deterministic models all on the same platform. This new approach allows you to truly understand your options as you explore the best plan for your business and develop a robust and repeatable process.
Load testing is an important part of the performance engineering process. However the industry is changing and load testing should adjust to these changes - a stereotypical, last-moment performance check is not enough anymore. There are multiple aspects of load testing - such as environment, load generation, testing approach, life-cycle integration, feedback and analysis - and none remains static. This presentation discusses how performance testing is adapting to industry trends to remain relevant and bring value to the table.
How to Make a Field invisible in Odoo 17Celine George
It is possible to hide or invisible some fields in odoo. Commonly using “invisible” attribute in the field definition to invisible the fields. This slide will show how to make a field invisible in odoo 17.
Francesca Gottschalk - How can education support child empowerment.pptxEduSkills OECD
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Macroeconomics- Movie Location
This will be used as part of your Personal Professional Portfolio once graded.
Objective:
Prepare a presentation or a paper using research, basic comparative analysis, data organization and application of economic information. You will make an informed assessment of an economic climate outside of the United States to accomplish an entertainment industry objective.
The Roman Empire A Historical Colossus.pdfkaushalkr1407
The Roman Empire, a vast and enduring power, stands as one of history's most remarkable civilizations, leaving an indelible imprint on the world. It emerged from the Roman Republic, transitioning into an imperial powerhouse under the leadership of Augustus Caesar in 27 BCE. This transformation marked the beginning of an era defined by unprecedented territorial expansion, architectural marvels, and profound cultural influence.
The empire's roots lie in the city of Rome, founded, according to legend, by Romulus in 753 BCE. Over centuries, Rome evolved from a small settlement to a formidable republic, characterized by a complex political system with elected officials and checks on power. However, internal strife, class conflicts, and military ambitions paved the way for the end of the Republic. Julius Caesar’s dictatorship and subsequent assassination in 44 BCE created a power vacuum, leading to a civil war. Octavian, later Augustus, emerged victorious, heralding the Roman Empire’s birth.
Under Augustus, the empire experienced the Pax Romana, a 200-year period of relative peace and stability. Augustus reformed the military, established efficient administrative systems, and initiated grand construction projects. The empire's borders expanded, encompassing territories from Britain to Egypt and from Spain to the Euphrates. Roman legions, renowned for their discipline and engineering prowess, secured and maintained these vast territories, building roads, fortifications, and cities that facilitated control and integration.
The Roman Empire’s society was hierarchical, with a rigid class system. At the top were the patricians, wealthy elites who held significant political power. Below them were the plebeians, free citizens with limited political influence, and the vast numbers of slaves who formed the backbone of the economy. The family unit was central, governed by the paterfamilias, the male head who held absolute authority.
Culturally, the Romans were eclectic, absorbing and adapting elements from the civilizations they encountered, particularly the Greeks. Roman art, literature, and philosophy reflected this synthesis, creating a rich cultural tapestry. Latin, the Roman language, became the lingua franca of the Western world, influencing numerous modern languages.
Roman architecture and engineering achievements were monumental. They perfected the arch, vault, and dome, constructing enduring structures like the Colosseum, Pantheon, and aqueducts. These engineering marvels not only showcased Roman ingenuity but also served practical purposes, from public entertainment to water supply.
Embracing GenAI - A Strategic ImperativePeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
Palestine last event orientationfvgnh .pptxRaedMohamed3
An EFL lesson about the current events in Palestine. It is intended to be for intermediate students who wish to increase their listening skills through a short lesson in power point.
This talk provides an overview of the basic steps needed to produce a project plan. The framework provided should allow students to identify where some of the particular issues discussed in other chapters are applied to the planning process. As the focus is on project planning, techniques to do with project control are not explicitly described. However, in practice, one element of project planning will be to decide what project control procedures need to be in place.
A key point here is that developers may in fact be very competent, but incorrect estimates leading to unachievable targets will lead to extreme customer dissatisfaction.
The answer to the problem of over-optimistic estimates might seem to be to pad out all estimates, but this itself can lead to problems. You might miss out to the competition who could underbid you, if you were tendering for work. Generous estimates also tend to lead to reductions in productivity. On the other hand, having aggressive targets in order to increase productivity could lead to poorer product quality.
Ask how many students have heard of Parkinson’s Law – the response could be interesting! It is best to explain that C. Northcote Parkinson was to some extent a humourist, rather than a heavyweight social scientist.
Note that ‘zeroth’ is what comes before first.
This is discussed in Section 5.3 of the text which also covers Brooks’Law.
This taxonomy is based loosely on Barry Boehm’s in the big blue book, ‘Software Engineering Economics’.
One problem is that different people call these approaches by different names. In the case of bottom-up and analogy some of the alternative nomenclatures have been listed.
‘Parkinson’ is setting a target based on the amount of staff effort you happen to have available at the time. ‘Price to win’ is setting a target that is likely to win business when tendering for work. Boehm is scathing about these as methods of estimating. However, sometimes you might have to start with an estimate of an acceptable cost and then set the scope of the project and the application to be built to be within that cost.
This is discussed in Section 5.5 of the textbook.
There is often confusion between the two approaches as the first part of the bottom-up approach is a top-down analysis of the tasks to be done, followed by the bottom-up adding up of effort for all the work to be done. Make sure you students understand this or it will return to haunt you (and them) at examination time.
The idea is that even if you have never done something before you can imagine what you could do in about a week.
Exercise 5.2 relates to bottom-up estimating
The problems with COCOMO is that the input parameter for system size is an estimate of lines of code. This is going to have to be an estimate at the beginning of the project.
Function points, as will be seen, counts various features of the logical design of an information system and produced an index number which reflects the amount of information processing it will have to carry out. This can be crudely equated to the amount of code it will need.
It would be worth getting the students to do Exercise 5.3 at this point to make sure they grasp the underlying concepts.
This is analogous to calculating speed from distance and time.
‘System size’ here can be seen as an index that allows the size of different applications to be compared. It will usually correlate to the number of lines of code required.
COCOMO originally was based on a size parameter of lines of code (actually ‘thousand of delivered sourcecode instructions’ or kdsi). Newer versions recognize the use of functions points as a size measure, but convert them to a number called ‘equivalent lines of code (eloc).
Once again, just a reminder that the lecture is just an overview of concepts. Mark II FPs is a version of function points developed in the UK and is only used by a minority of FP specialists. The US-based IFPUG method (developed from the original Albrecht approach) is more widely used. I use the Mark II version because it has simpler rules and thus provides an easier introduction to the principles of FPs. Mark II FPs are explained in more detail in Section 5.9. If you are really keen on teaching the IFPUG approach then look at Section 5.10. The IFPUG rules are really quite tricky in places and for the full rules it is best to contact IFPUG.
For each transaction (cf use case) count the number of input types (not occurrences e.g. where a table of payments is input on a screen so the account number is repeated a number of times), the number of output types, and the number of entities accessed. Multiply by the weightings shown and sum. This produces an FP count for the transaction which will not be very useful. Sum the counts for all the transactions in an application and the resulting index value is a reasonable indicator of the amount of processing carried out. The number can be used as a measure of size rather than lines of code. See calculations of productivity etc discussed earlier.
There is an example calculation in Section 5.9 (Example 5.3) and Exercise 5.7 should give a little practice in applying the method.
Attempts have been made to extend IFPUG FPs to real-time and embedded systems, but this has not been very convincing (IMHO).
Embedded software component is seen as at a particular level in the system. It receives calls for services from a higher layer and requests services from lower layers. It will receive responses from lower levels and will send responses to higher levels.
Each arrow represents an enter or exit if in black, or a read/write if in red.
Exercise 5.8 gives some practice in applying the technique.
Recall that the aim of this lecture is to give an overview of principles. COCOMO81 is the original version of the model which has subsequently been developed into COCOMO II some details of which are discussed in Section 5.12. For full details read Barry Boehm et al. Software estimation with COCOMO II Prentice-Hall 2002.
An interesting question is what a ‘semi-detached’ system is exactly. To my mind, a project that combines elements of both real-time and information systems (i.e. has a substantial database) ought to be even more difficult than an embedded system.
Another point is that COCOMO was based on data from very large projects. There are data from smaller projects that larger projects tend to be more productive because of economies of scale. At some point the diseconomies of scale caused by the additional management and communication overheads then start to make themselves felt.
Exercise 5.10 in the textbook provides practice in applying the basic model.
Virtual machine volatility is where the operating system that will run your software is subject to change. This could particularly be the case with embedded control software in an industrial environment.
Schedule constraints refers to situations where extra resources are deployed to meet a tight deadline. If two developers can complete a task in three months, it does not follow that six developers could complete the job in one month. There would be additional effort needed to divide up the work and co-ordinate effort and so on.
Exercise 5.11 gives practice in applying these.
The source cases, in this situation, are completed projects. For each of details of the factors that would have a bearing on effort are recorded. These might include lines of code, function points (or elements of the FP counts such as the number of inputs, outputs etc), number of team members etc etc. For the values for the new project are used to find one or more instances from the past projects than match the current one. The actual effort from the past project becomes the basis of the estimate for the new project.