Phase Distribution of Software Development Effort

1. Phase Distribution of Software Development Effort Ye Yang, Mei He, Mingshu Li, Qing Wang, Barry Boehm Institute of Software,Chinese Academy of Sciences (ISCAS) & USC-CSSE COCOMO Forum’08 October 28, 2008

2. Outline Background Subject and approach Results Discussions and Conclusions 2

3. Background 1950’s: Norden observed Rayleigh Curve to be a good staffing level • Approximation to most hardware development projects • Main criticism: slow early build-up and long-tail effects COCOMO 81, the Detailed COCOMO Model • Phase-sensitive cost multipliers enable more accurate, phase-wise estimation • Allows to track down the effect of individual cost driver rating on phase distribution variation COCOMO 2000 • Phase neutral cost multipliers due to lack of calibration data • Two simplified schemes to facilitate phase/activity distribution: waterfall & RUP Challenges • Lack of studies on causes of distribution variation • Lot of estimation methods but lack of insightful allocation guidelines Our study • Empirical analysis towards developing more in-depth understanding on factors impacting on the degree of intensity of different development phases/activities 3

4. Recent Studies Heijstek and Chaudron (2007) • Analyzed data from model-based development projects • Confirmed the similarity with RUP hump-chart Milicic and Wholin (2004) • Studied characteristics affecting estimating accuracy & proposed a lean approach to improve estimation based on distribution patterns of estimation errors Yiftachel et al. (2006) • Proposed an economic model for optimal allocation of resources among development phases Lucia et al. (2003), Yang et al. (2008): • High correlation between the effort of subsequent activities for maintenance projects 4

5. Different Phase Definitions 5

6. Outline Background Subject and approach Results Discussions and Conclusions 6

7. Subject Roughly waterfall; transforming guidelines were provided during data collection CSBSG (China Software Benchmarking Standard Group) • Established in 2006 • Consistent with ISBSG • Supported by the Government and software industry association of China • 1012 projects from 141 organizations and 15 regions Phase/Activity definition in CSBSG database: 7

8. Approach of the study Data Collection • Web-based questionnaire • Sent out by CSBSG staff, completed by leading companies • Simple automatic checking spelling and inconsistency errors • Expert group check • Determined for data quality and candidacy Data Selection and Cleaning • Select the minimum set of attributes considered in the study • Clean the data through several steps 8

9. Summary of Attributes Considered 9

10. Data Selection 10

11. Outline Background Subject and approach Results Discussions and Conclusions 11

12. Summary of the project data 12

13. Dataset Comparison 15.2 LOC/man-hour 1.2 LOC/man-hour

14. Overall phase distribution pattern Comparison of CSBSG and COCOMO II Distribution Overall phase distribution profile 14

15. Comparison by life cycle models Classifications: waterfall, iterative, rapid prototyping # of projects: 57, 15, 2 respectively Mean software size (KSLOC): Waterfall: 103 Iterative: 259 Rapid: 89

16. Comparison by Development type • Enhancement projects have more focus on Test phase, • Re-development has the greatest emphasis on Code phase 16

17. Comparison by Development type • Totally, New development has lower estimation error • All phased effort for Code are underestimated, and for Transition, over-estimated Comparison of estimation accuracy RE = (Estimated phase effort – Actual phase effort)/Actual phase effort 17

18. Comparison by Size scale Distribution of software size 18

19. Comparison by Size scale • Ascending trend for Code • Descending trend for Test Comparison among difference software sizes scale in LOC 19

20. Comparison by Team Size Average phase distribution for different team size 20

21. Outline Background Subject and approach Results Discussions and Conclusions 21

22. Factors influencing phased distribution ANOVA analysis is used to examine to what a degree the variance of each phase effort distribution percentage is explained by class variables 22

23. Guidelines for phased distribution Analysis on determining reasonable phase effort distribution quantities should be performed in the cost estimation process. CSBSG data analysis shows a waterfall-based phase distribution scheme as: 16.14% for plans and requirements phase, 14.88% for design phase, 40.36% for code phase, 21.57% for test phase, and the other 7.06% for transition phase. Software size and development type are two major factors to be considered when adjusting effort allocation. CSBSG dataset shows that for enhancement type of projects, percentage of development effort in code phase decreases by 10.67%, and that for test phase increases by 5.4%. CSBSG dataset indicates that as software size dramatically grows, distribution has an intensive focus on both code and test phases. CSBSG dataset indicates that team size is not a significant factor which may cause phase effort distribution variation. However, it shows a distinguishable emphasis on Test phase with the growth of team size 23

24. Limitations Applicability of empirical findings • All projects from domestic organizations in China Size metric • SLOC vs. FP Selected influencing factors • Correlation among factors not considered 24

25. Conclusions and Future Work Empirical analysis towards developing more in-depth understanding on factors impacting on the phases/activities distribution Providing empirically-based suggestions for Chinese software industry Next steps • Consider other influencing factors such as system complexity, number of concurrent users • Strengthening the findings through more thorough statistical analysis 25

26. Thank You! Contact info: ye@itechs.iscas.ac.cn