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
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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
Cocomo 81 e.g. more focus on integration and test phase and less on code as the software size grows (i.e. 16% for small projects and 25% for large projects)
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4. Recent Studies Heijstek and Chaudron (2007) Milicic and Wholin (2004)
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
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5. Different Phase Definitions
Estimation
Model
Phases covered
COCOMO 81
Plan and requirement, preliminary design, detailed design (25%), code (33%), Integration & Test (25%)
COCOMO II
Option 1(Waterfall): Plan and requirement (7%), preliminary design (17%), detailed design (25%), code (33%), Integration & Test (25), deployment & maintenance (12%);
Option 2(MBASE/RUP): Inception(6%), Elaboration(24%), Construction(76%), Transition (12%)
RUP
Inception(5%), Elaboration(20%), Construction(65%), Transition(10%)
COCOTS
Assessment, Tailoring, Gluecode & Integration.
SLIM
Concept Definition, Requirement & Design, Construct & Test, Perfective Maintenance
SEER-SEM
Early specification, design, development, delivery & maintenance
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6. Outline Background Subject and approach Results
Discussions and Conclusions
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7. Subject 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:
Roughly waterfall; transforming guidelines were provided during data collection
Phase
Activities Included
Plan
Plan, Preliminary Requirement Analysis
Requirement
Requirement Analysis
Design
Product Design, Detailed Design
Code
Code, Unit Test, Integration
Test
System Test
Transition
Installation, Transition, Acceptance Test, User Training, Support
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8. Approach of the study Data Collection Data Selection and Cleaning
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
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9. Summary of Attributes Considered
Metric
Unit
Description
Size
SLOC
Total Lines of Code
Effort
Person-Hour
Summary Work Effort
Plan phase effort
Work Effort of plan phase
Reqt’s phase effort
Work Effort of reqt’s phase
Design phase effort
Work Effort of design phase
Code phase effort
Work Effort of code phase
Test phase effort
Work Effort of test phase
Transition phase effort
Work Effort of transition phase
Development life cycle
Nominal
Waterfall, iterative, rapid prototyping
Team Size
# Person
Maximum size of the development team
Development Type
New development, Enhancement, Re-development
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10. Data Selection Step ID # of proj. excluded # of proj. remained
Reason for exclusion 1
115
Do not contain phased-related effort records 2 14
101
there are two or more phases’ effort data missing 3
100
the sum of its 5 recorded phased effort is greater than the total effort 4
if only 1 of the 5 phase effort value is missing, it is filled up by subtracting the other 4 phases from the total effort 5 25 75
one or more than one phase’s effort data recorded as zero
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11. Outline Background Subject and approach Results
Discussions and Conclusions
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12. Summary of the project data
Mean
Median
Min
Max
Size (KSLOC)
136.4
45.7
0.77
2340
Effort (Person-hours)
8969
4076
568
134840
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13. Dataset Comparison Mean Median Min Max CSBSG Size (KSLOC) 136.4 45.7
0.77
2340
Effort (Person-hours)
8969
4076
568
134840
COCOMO
Size(KSLOC)
127 47 3
1293
Effort(Person-Months)
714
195 6
11400
Effort(Person-hours)
108528
29640
912
15.2 LOC/man-hour
1.2 LOC/man-hour
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14. Overall phase distribution pattern
Overall phase distribution profile
Comparison of CSBSG and COCOMO II Distribution
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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
Explanation from the paper
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16. Comparison by Development type
Enhancement projects have more focus on Test phase,
Re-development has the greatest emphasis on Code phase
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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
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18. Comparison by Size scale
Distribution of software size
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19. Comparison by Size scale
Ascending trend for Code
Descending trend for Test
Comparison among difference software sizes scale in LOC
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20. Comparison by Team Size
Average phase distribution for different team size
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21. Outline Background Subject and approach Results
Discussions and Conclusions
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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
Phase Distribution
DevType
Software size
Team size
P&R%
3.96%
14.80%
5.50%
Design%
2.57%
3.67%
0.62%
Code%
12.22%
7.93%
0.02%
Test%
7.47%
7.16%
3.05%
Trans.%
2.72%
9.36%
1.52%
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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
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24. Limitations Applicability of empirical findings Size metric
All projects from domestic organizations in China
Size metric
SLOC vs. FP
Selected influencing factors
Correlation among factors not considered
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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
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26. Thank You!
Contact info:
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