Software Cost Estimation

1. Software Cost Estimation Seth Bowen Samuel Lee Lance Titchkosky

2. Outline • Introduction • Inputs and Outputs • Methods of Estimation • COCOMO • Conclusion

3. Cost Estimation Is Needed • 55% of projects over budget • 24 companies that developed large distributed systems (1994) • 53% of projects cost 189% more than initial estimates • Standish Group of 8,380 projects (1994)

4. Cost Estimation • An approximate judgment of the costs for a project • Many variables • Often measured in terms of effort (i.e., person months/years) • Different development environments will determine which variables are included in the cost value • Management costs • Development costs • Training costs • Quality assurance • Resources

5. Cost Estimation Affects • Planning and budgeting • Requirements prioritization • Schedule • Resource allocation • Project management • Personnel • Tasks

6. Cost Estimation During the Software Life Cycle • Cost estimation should be done throughout the software life cycle to allow for refinement • Need effective monitoring and control of the software costs to verify and improve accuracy of estimates • At appropriate level of detail • Gathering data should not be difficult • Success of a cost estimate method is not necessarily the accuracy of the initial estimates, but rather the rate at which estimates converge to the actual cost

7. Who is the Estimator? • Someone responsible for the implementation • Can compare previous projects in organization to current project • Usually experienced • Someone from outside the organization • Can provide unbiased estimate • Tend to use empirical methods of estimation • Difficulties: • Lack of confidence that a model will outperform an expert • Lack of historical data to calibrate the model

8. General Steps and Remarks • Establish Plan • What data should we gather • Why are we gathering this data • What do we hope to accomplish • Do cost estimation for initial requirements • Decomposition • Use several methods • There is no perfect technique • If get wide variances in methods, then should re-evaluate the information used to make estimates

9. General Steps and Remarks (cont.) • Do re-estimates during life cycle • Make any required changes to development • Do a final assessment of cost estimation at the end of the project

10. Software Cost Estimation Process • Definition • A set of techniques and procedures that is used to derive the software cost estimate • Set of inputs to the process and then the process will use these inputs to generate the output

11. Inputs and Outputs to the Estimation Process Classical view of software estimation process [Vigder/Kark]

12. Inputs and Outputs to the Estimation Process (Cont.) Actual cost estimation process [Vigder/Kark]

13. Cost Estimation Accuracy • To determine how well a cost estimation model predicts • Assessing model performance • Absolute Error = (Epred – Eact) • Percentage Error = (Epred – Eact) / Eact • Mean Magnitude of Relative Error

14. Cost Estimation Methods • Algorithmic (Parametric) model • Expert Judgment (Expertise Based) • Top – Down • Bottom – Up • Estimation by Analogy • Price to Win Estimation

15. Algorithmic (Parametric) Model • Use of mathematical equations to perform software estimation • Equations are based on theory or historical data • Use input such as SLOC, number of functions to perform and other cost drivers • Accuracy of model can be improved by calibrating the model to the specific environment

16. Algorithmic (Parametric) Model (Cont.) • Examples: • COCOMO (COnstructive COst MOdel) • Developed by Boehm in 1981 • Became one of the most popular and most transparent cost model • Mathematical model based on the data from 63 historical software project • COCOMO II • Published in 1995 • To address issue on non-sequential and rapid development process models, reengineering, reuse driven approaches, object oriented approach etc • Has three submodels – application composition, early design and post-architecture

17. Algorithmic (Parametric) Model (Cont.) • Putnam’s software life-cycle model (SLIM) • Developed in the late 1970s • Based on the Putnam’s analysis of the life-cycle in terms of a so-called Rayleigh distribution of project personnel level versus time. • Quantitative software management developed three tools : SLIM-Estimate, SLIM-Control and SLIM-Metrics.

18. Algorithmic (Parametric) Model (Cont.) • Advantages • Generate repeatable estimations • Easy to modify input data • Easy to refine and customize formulas • Objectively calibrated to experience • Disadvantages • Unable to deal with exceptional conditions • Some experience and factors can not be quantified • Sometimes algorithms may be proprietary

19. Expert Judgment • Capture the knowledge and experience of the practitioners and providing estimates based upon all the projects to which the expert participated. • Examples • Delphi • Developed by Rand Corporation in 1940 where participants are involved in two assessment rounds. • Work Breakdown Structure (WBS) • A way of organizing project element into a hierarchy that simplifies the task of budget estimation and control

20. Expert Judgment (Cont.) • Advantages • Useful in the absence of quantified, empirical data. • Can factor in differences between past project experiences and requirements of the proposed project • Can factor in impacts caused by new technologies, applications and languages. • Disadvantages • Estimate is only as good expert’s opinion • Hard to document the factors used by the experts

21. Top - Down • Also called Macro Model • Derived from the global properties of the product and then partitioned into various low level components • Example – Putnam model

22. Top – Down (Cont.) • Advantages • Requires minimal project detail • Usually faster and easier to implement • Focus on system level activities • Disadvantages • Tend to overlook low level components • No detailed basis

23. Bottom - Up • Cost of each software components is estimated and then combine the results to arrive the total cost for the project • The goal is to construct the estimate of the system from the knowledge accumulated about the small software components and their interactions • An example – COCOMO’s detailed model

24. Bottom – Up (Cont.) • Advantages • More stable • More detailed • Allow each software group to hand an estimate • Disadvantages • May overlook system level costs • More time consuming

25. Estimation by Analogy • Comparing the proposed project to previously completed similar project in the same application domain • Actual data from the completed projects are extrapolated • Can be used either at system or component level

26. Estimation by Analogy (Cont.) • Advantages • Based on actual project data • Disadvantages • Impossible if no comparable project had been tackled in the past. • How well does the previous project represent this one

27. Price to Win Estimation • Price believed necessary to win the contract • Advantages • Often rewarded with the contract • Disadvantages • Time and money run out before the job is done

28. COCOMO 81 • COCOMO stands for COnstructive COst MOdel • It is an open system First published by Dr Barry Bohem in 1981 • Worked quite well for projects in the 80’s and early 90’s • Could estimate results within ~20% of the actual values 68% of the time

29. COCOMO 81 • COCOMO has three different models (each one increasing with detail and accuracy): • Basic, applied early in a project • Intermediate, applied after requirements are specified. • Advanced, applied after design is complete • COCOMO has three different modes: • Organic – “relatively small software teams develop software in a highly familiar, in-house environment” [Bohem] • Embedded – operate within tight constraints, product is strongly tied to “complex of hardware, software, regulations, and operational procedures” [Bohem] • Semi-detached – intermediate stage somewhere between organic and embedded. Usually up to 300 KDSI

30. COCOMO 81 • COCOMO uses two equations to calculate effort in man months (MM) and the number on months estimated for project (TDEV) • MM is based on the number of thousand lines of delivered instructions/source (KDSI) • MM = a(KDSI)b * EAF • TDEV = c(MM)d • EAF is the Effort Adjustment Factor derived from the Cost Drivers, EAF for the basic model is 1 • The values for a, b, c, and d differ depending on which mode you are using

31. COCOMO 81 • A simple example: Project is a flight control system (mission critical) with 310,000 DSI in embedded mode • Reliability must be very high (RELY=1.40).  So we can calculate: • Effort = 1.40*3.6*(319)1.20 = 5093 MM • Schedule = 2.5*(5093)0.32 = 38.4 months • Average Staffing = 5093 MM/38.4 months = 133 FSP

32. COCOMO II • Main objectives of COCOMO II: • To develop a software cost and schedule estimation model tuned to the life cycle practices of the 1990’s and 2000’s • To develop software cost database and tool support capabilities for continuous model improvement • From “Cost Models for Future Software Life Cycle Processes: COCOMO 2.0," Annals of Software Engineering, (1995).

33. COCOMO II Has three different models: • The Application Composition Model • Good for projects built using rapid application development tools (GUI-builders etc) • The Early Design Model • This model can get rough estimates before the entire architecture has been decided • The Post-Architecture Model • Most detailed model, used after overall architecture has been decided on

34. COCOMO II Differences • The exponent value b in the effort equation is replaced with a variable value based on five scale factors rather then constants • Size of project can be listed as object points, function points or source lines of code (SLOC). • EAF is calculated from seventeen cost drivers better suited for today's methods, COCOMO81 has fifteen • A breakage rating has been added to address volatility of system

35. COCOMO II Calibration • For COCOMO II results to be accurate the model must be calibrated • Calibration requires that all cost driver parameters be adjusted • Requires lots of data, usually more then one company has • The plan was to release calibrations each year but so far only two calibrations have been done (II.1997, II.1998) • Users can submit data from their own projects to be used in future calibrations

36. Importance of Calibration • Proper calibration is very important • The original COCOMO II.1997 could estimate within 20% of the actual values 46% of the time. This was based on 83 data points. • The recalibration for COCOMO II.1998 could estimate within 30% of the actual values 75% of the time. This was based on 161 data points.

37. Is COCOMO the Best? • COCOMO is the most popular method however for any software cost estimation you should really use more then one method • Best to use another method that differs significantly from COCOMO so your project is examined from more then one angle • Even companies that sell COCOMO based products recommend using more then one method. Softstar (creators of Costar) will even provide you with contact information for their competitor’s products

38. COCOMO Conclusions • COCOMO is the most popular software cost estimation method • Easy to do, small estimates can be done by hand • USC has a free graphical version available for download • Many different commercial version based on COCOMO – they supply support and more data, but at a price

39. Conclusions • Project costs are being poorly estimated • The accuracy of cost estimation has to be improved • Data collection • Use of tools • Use several methods of estimation

40. References • Boehm B., Clark B., Horowitz E., Madachy R., Shelby R., Westland C. (1995). Cost Models for Future Software Life Cycle Processes: COCOMO 2.0, Annals of Software Engineering. http://sunset.usc.edu/research/COCOMOII/Docs/stc.pdf. • Boehm B., Clark B., Horowitz E., Madachy R., Shelby R., Westland C. (1995). An Overview of the COCOMO 2.0 Software Cost Model. http://sunset.usc.edu/research/COCOMOII/Docs/stc.pdf. • Boehm B., Chulani S., Clark B. (1997). Calibration Results of COCOMO II.1997. http://sunset.usc.edu/publications/TECHRPTS/1998/usccse98-502/CalPostArch.pdf. • Boehm B., Chulani S., Clark B. (1997). Calibrating the COCOMO II Post Architecture Model. http://sunset.usc.edu/Research_Group/Sunita/down/calpap.pdf. • Boehm B., Chulani S., Reifer D., The Rosetta Stone: Making COCOMO 81 Files Work With COCOMO II. http://sunset.usc.edu/publications/TECHRPTS/1998/usccse98-516/usccse98-516.pdf. • Chulani, S. (1998). Software Development Cost Estimation Approaches – A Survey. IBM Research. • Humphrey, W.S. (1990). Managing the Software Process. Addison-Wesley Publishing Company, New York, NY.

41. References • Hussein, A. (2001). Introduction to Software Process Management. University of Calgary, Calgary, Canada. http://sern.ucalgary.ca/courses/SENG/621/W01/intro.ppt. • Londeix, B. (1987). Cost Estimation for Software Development. Addison-Wesley Publishing Company, New York, NY. • Pressman, R.S. (2001). Software Engineering: A Practitioner’s Approach. McGraw-Hill Higher Education, New York, NY. • Vigder, M. R. and Kark, A. W. (1994). Software Cost Estimation and Control. Software Engineering Institute for Information Technology. http://wwwsel.iit.nrc.ca/seldocs/cpdocs/NRC37116.pdf. • Wu, L. (1997). The comparison of the Software Cost Estimating Methods. University of Calgary, Calgary, Canada. http://sern.ucalgary.ca/courses/seng/621/W97/wul/seng621_11.html. • Basic COCOMO Software Cost Model. http://www.jsc.nasa.gov/bu2/COCOMO.html. • COCOMO 2, Softstar Systems. http://www.softstarsystems.com/cocomo2.htm. • Answers to Frequently Asked Questions, Softstar Systems. http://www.softstarsystems.com/faq.htm.

42. Questions and Discussion