Productivity Data Analysis and Issues

1. Productivity Data Analysis and Issues Brad Clark, Thomas Tan USC CSSE Annual Research Review March 8, 2010

2. Table of Contents • Background • Productivity Data Analysis by Application Domain • Reducing the number of domains: Application Difficulty • Topics for further discussion This work is sponsored by the Air Force Cost Analysis Agency USC CSSE Annual Research Review - Mar 2010

3. Background • DoD has been collecting Software resource data for a number of years • Product and development description • Product size • Resources and schedule • Product quality • Analyzing ~140 records out of ~300 • Additional data is coming in • Objective: Improved cost estimation of future DoD software-intensive systems, as well as to the DoD cost community. • Characterize different Application Domains within DoD • Analyze collected data for simple cost estimating relationships within each domain • Develop rules-of-thumb for missing data • Make collected data useful to oversight and management entities USC CSSE Annual Research Review - Mar 2010

4. Software Resources Data Report USC CSSE Annual Research Review - Mar 2010

5. SRDR Data Missing Domains: Internet, Maintenance and Diagnostics, Spacecraft bus Notes: SRDR: Software Resources Data Report USC CSSE Annual Research Review - Mar 2010

6. Preliminary Results - Do Not Use! USC CSSE Annual Research Review - Mar 2010

7. Simple Cost Estimating Relationships PM = A * (EKSLOC)B Preliminary Results - Do Not Use! Notes: PM: Person Months (152 labor hours / month) EKSLOC: Equivalent Thousands of Source Lines of Code USC CSSE Annual Research Review - Mar 2010

8. Sizing Issues -1 • Multiple SLOC counting methods • Physical: total number of lines in a file • Non-commented Source: no blank or comment lines • Logical • No Deleted Code Counts • SLOC Conversion Experiment • Use the results of USC’s Code Count Tool to find conversion ratios • Physical to Logical • NCSS to Logical • Results segregated by programming language USC CSSE Annual Research Review - Mar 2010

9. NCSS to Logical Conversion Ada: 45% C/C++: 61% C#: 61% Java: 72% USC CSSE Annual Research Review - Mar 2010

10. Sizing Issues -2 • No Modified Code parameters • Percent Design Modified (DM) • Percent Code Modified (CM) • Percent Integration and Test Modified (IM) • Software Understanding (SU) • Programmer Unfamiliarity (UNFM) • Program interviews provided parameters for some records USC CSSE Annual Research Review - Mar 2010

11. Effort Issues • Missing effort reporting for different lifecycle phases • Software requirements analysis (REQ) • Software architectural design (ARCH) • Software coding and testing (CODE) • Software integration (INT) • Software qualification testing (QT) • Software management, CM, QA, etc. (Other – very inconsistent) USC CSSE Annual Research Review - Mar 2010

12. Collapsing Application Domains • Propose to reduce the number of application domains • Currently have a “sparse” data table • Use a model-independent approach • 5-level scale to capture the “difficulty” (and therefore impact) of an application domain on productivity USC CSSE Annual Research Review - Mar 2010

13. Software Application Difficulties Difficulty would be described in terms of required software reliability, database size, product complexity, integration complexity, information assurance, real-time requirements, different levels of developmental risks, etc. USC CSSE Annual Research Review - Mar 2010

14. Application Difficulty Issues USC CSSE Annual Research Review - Mar 2010

15. Questions? For more information, contact: Thomas Tan thomast@usc.edu 626-617-1128 Or Brad Clark bkclark@usc.edu 703-754-0115 USC CSSE Annual Research Review - Mar 2010