Predicting Understandability of a Software Project Using COCOMO II Model Drivers - PowerPoint PPT Presentation

Predicting Understandability of a Software Project Using COCOMO II Model Drivers

Ali Afzal Malik

Barry Boehm

A. Winsor Brown

{alimalik, boehm, awbrown} @usc.edu

23rd International Forum on COCOMO and Systems/Software Cost Modeling

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• Introduction
• Motivation & Related Work
• Methodology
• Results
• Future Work
• References

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• Understandability
• “Degree of clarity of the purpose and requirements of a software system to the developers of that system at the end of the Inception phase”
• Basic idea
• Quantification enables prediction
• Reuse inputs of software cost estimation
• Empirical Study
• Projects

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(Kruchten, 2003)

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• SE I (Fall) and SE II (Spring)
• 2004 – 2007
• 24 real-client, MS-student, team projects (SE I 2008, SE II 2008)
• Process: MBASE/RUP (Boehm et al. 2005, Kruchten 2003)
• Projects
• Development-intensive
• Used COCOMO II

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• Some important considerations
• 18% of software project failures due to unclear objectives and incomplete R&S (Standish Group 1995)
• Escalation in cost of fixing requirements defects: rapid for large and considerable for smaller projects (Boehm 1981, Boehm and Turner 2004)
• Requirement changes have significant impact on project’s budget and schedule (Zowghi and Nurmuliani 2002)

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• An objective mechanism to predict understandability enables
• Minimization of resource wastage due to rework
• Answering “How much RE is enough?”
• Related previous work
• “Expert COCOMO” (Madachy 1997)
• Uses COCOMO II cost factors to quantify risk

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• Identified 8 relevant COCOMO II model drivers

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• Weighted-sum formula
• UNDR – understandability
• MDi – ith Model Driver’s value
• wi – weight of MDi
• ni – nature of MDi; Є{-1, +1}
• -1 for CPLX; +1 for the rest

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• Model driver rating scale

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• Voting for model driver weights
• 22 students from SE II class
• Rating scale
• 1 (least important) – 5 (most important)

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• Determine the lowest and highest numerical values of understandability

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• min() and max()

min (MDi) {

if (ni = = +1) return minimum numerical value of MDi

else return maximum numerical value of MDi

}

max (MDi) {

if (ni = = +1) return maximum numerical value of MDi

else return minimum numerical value of MDi

}

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VUA

ICA

WUA

2.86

44.12

85.38

126.64

UNDRLow

UNDRHigh

• Project categories using UNDR ranges
• Vaguely-understood applications (VUA)
• Intermediate-clarity applications (ICA)
• Well-understood applications (WUA)

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• Ranges defining VUA, ICA, and WUA groups are adjustable
• End-points of spectrum depend on weights
• Range sizes can be non-uniform

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• Prediction accuracy
• ICA: 100%
• VUA: 50% (2 out of 4)
• WUA: 33% (1 out of 3)
• Overall: 83% (20 out of 24)
• Possible reasons for discrepancies
• Inappropriate COCOMO II model drivers
• Voters are different from developers
• Projects # 5 & 22

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• Weights for commercial projects using techniques such as Wideband Delphi (Boehm 1981)
• Investigate other widely-used models e.g. SLIM (Putnam 1978) and PRICE-S (Freiman and Park 1979)
• Analyze understandability’s contribution towards degree of requirements elaboration (Malik and Boehm 2008)

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• Books
• Boehm, B., Software Engineering Economics, Prentice-Hall, 1981.
• Boehm, B., Abts, C., Brown, A., Chulani, S., Clark, B, Horowitz, E., Madachy, R., Reifer, D., and Steece, B., Software Cost Estimation with COCOMO II, Prentice Hall, 2000.
• Boehm, B. and Turner, R., Balancing Agility and Discipline: A Guide for the Perplexed, Addison-Wesley, 2004.
• Kruchten, P., The Rational Unified Process: An Introduction, Addison-Wesley, 2003.
• Conference papers
• Boehm, B., “Anchoring the Software Process”, IEEE Software 13(4), 1996, pages 73-82.
• Freiman, F.R. and Park, R. E., “PRICE Software Model–Version 3: An Overview”, Proc. IEEE-PINY Workshop on Quantitative Software Models, 1979, pages 32-41.
• Madachy, R., “Heuristic Risk Assessment Using Cost Factors”, IEEE Software4 (3), 1997, pages 51-59.
• Malik, A. A. and Boehm, B., “An Empirical Study of Requirements Elaboration”, The 22nd Brazilian Symposium on Software Engineering (SBES’08), 2008.
• Putnam, L. H., “A General Empirical Solution to the Macro Software Sizing and Estimating Problem”, IEEE Trans. Software Engr., 1978, pages 345–361.
• Zowghi, D. and Nurmuliani, N., “A Study of the Impact of Requirements Volatility on Software Project Performance”, Proceedings of the Ninth Asia-Pacific Software Engineering Conference, 2002, pages 3-11.
• Miscellaneous
• Boehm, B., Klappholz, D., Colbert, E., et al., “Guidelines for Lean Model-Based (System) Architecting and Software Engineering (LeanMBASE)”, Center for Software Engineering, University of Southern California, 2005.
• SE I (2008). Links to websites of all past semesters of Software Engineering I (CSCI 577A) course at USC, http://sunset.usc.edu/csse/courseroot/course_list.html#577a
• SE II (2008). Links to websites of all past semesters of Software Engineering II (CSCI 577B) course at USC, http://sunset.usc.edu/csse/courseroot/course_list.html#577b
• Standish Group (1995). “CHAOS”, http://www.standishgroup.com

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