software cost model.pptx explains cost estimate
- 1. Topic Software Cost Model
- 2. Software Life Cycle Management
- 3. Project Cost Estimation Cost estimation in project management is the process of forecasting the effort, financial and other resources needed to complete a project within a defined scope.
- 4. Software Cost Model A technique/framework that is used to compute out the cost of a project or product. Many estimation models have been proposed over the last 30 years. Software cost estimation involves the determination of one or more of the following basic estimates: · Effort (usually in person-months) · Project duration (in calendar time) · Cost (in dollars)
- 5. Classic view of cost estimation process requirements Duration other cost drivers ( cost drivers) Cost Software Cost Estimation Technique Cost drivers Effort
- 6. Examples of cost drivers • 1. Product attributes a. Required software reliability b. Size of application database c. Complexity of the product 2.Hardware attributes a. Run-time performance constraints b. Memory constraints c. Volatility of the virtual machine environment d. Required turnabout time 3. Personnel attributes a. Analyst capability b. Software engineering capability c. Applications experience d. Virtual machine experience e. Programming language experience
- 7. 4. Project attributes a. Use of software tools b.Application of software engineering methods c.Required development schedule
- 8. Need of Cost Estimation Overestimation of cost • can easily lead to financial losses in any organization. Under-estimation of cost • can significantly contribute to poor quality service • delivery leading to failure of the entire project • leads to under-staffing • under-scoping the quality assurance effort • setting too short a schedule • resulting in loss of credibility as deadlines are missed. Accurate cost estimation:- • Project is completed on time • Project is completed within budget • Risk reduction
- 9. Types Of Models • Cost Estimation Models can be classified into 2 major categories: • Algorithmic Cost Estimation Models and • Non-algorithmic Cost Estimation Models
- 10. Algorithmic Models Algorithmic cost modelling uses a mathematical expression to predict project costs. These mathematical equations are based on research and historical data and use inputs such as Source Lines of Code (SLOC),number of functions to perform, and other cost drivers such as language, design methodology, skill-levels, risk assessments, company’s features etc.
- 11. Advantages of algorithmic models • It is able to generate repeatable estimation • Easy to modify input data • Easy to refine and customize formula • It is efficient and able to support a family of estimations or a sensitivity analysis. • It is objectively calibrated to previous experience.
- 12. Disadvantages of algorithmic model • Poor sizing inputs and inaccurate cost driver rating will result in inaccurate estimation. • It is difficult to estimate SIZE in the early stage of development • Estimates vary from one person to another person, depending on their background and experience with the type of system that is being developed. • Complex computation • A lot of information is needed to implement a model. • The number of lines of source code in software is the basic software metric used in many algorithmic cost models. The programming language used for system development also affects the number of lines of code to be implemented.
- 13. Types of Algorithmic techniques • Cocomo model • Putnam model • Function point based model
- 14. Cocomo Model:- It is regression based model. Pros: • gives clear results. • Simple to estimate cost and effort. Cons: • Much data is required • Estimation at the early stage of software development leads to failure.
- 15. Function Point based Model It allow the measurement of software size in standard units, independent of the underlying language in which the software is developed. Instead of counting the lines of code that make up a system, count the number of externals (inputs, outputs, inquiries, and interfaces) that make up the system. Pros:- • It can be estimated from requirements specifications. • It is possible to estimate development costs in the early phases of development. • Independent of the language, tools, or methodologies used for implementation. • Cons:- • It requires manual work, which require more time. • It requires detailed knowledge of requirement for estimation of software size using function points. • New developer cannot easily estimate the size as it requires • experience with function point.
- 16. Non-Algorithmic cost Estimation Model • Estimation can be done by using the previous projects previous experiences which is similar to the under estimate project. • Based on set of artificial intelligence techniques like neural networks , genetic Algorithm, rule based induction.
- 17. Examples of Non Algorithmic Models • Analogy • Expert judgement • Parkinson’s Law • Top Down • Bottom Up • Delphi • Pricingo to win
- 18. Analogy technique The cost of a new project is estimated by analogy with some completed projects. Pros:- • The estimator's past experience and knowledge can be used which is not easy to be quantified. • Easy to find differences between the completed and the proposed project can be identified and impacts estimated Cons • They are restricted to information that is available at the point that the prediction required. • Similar projects may not exist.
- 19. Expert judgement It involves an expert or group of experts consulting with software cost estimation. They shares experiences and understanding of the proposed project to reach at the estimate of project. Pros:- • Experts can tell the impact caused by the new technique,architecture,language etc. • Fast estimation. Cons:- • It is hard to document the factors used by the experts or experts-group.
- 20. Advantages of Non-Algorithmic Models • Non-algorithmic methods are easy to learn because all of them follow the human behavior. • It is possible to estimate development costs in the early phases of development. • Fast estimation.
- 21. Disadvantages of non algorithmic • This approach is not repeatable and the means of deriving an estimate are not explicit. • It is difficult to find highly experienced estimators for every new project. • The relationship between cost and system size is not linear. Cost tends to increase exponentially with size. • Budget manipulations by management aimed at avoiding overrun make experience and data from previous projects questionable
- 22. Opinion • Course was quite right according to online learning i.e. it didn't make us over burden nor tension free but kept us to learn within given resources. • Whatever was taught was learnt and understood.
- 23. Suggestion • Being the students of online classes we didn’t do any practical work, we should be given access to the technology by visiting atleast once in a month. • Online group discussion between students should had been arranged for assignments etc.