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Productivity Data Analysis and Issues

Productivity Data Analysis and Issues. Brad Clark, Thomas Tan USC CSSE Annual Research Review March 8, 2010. Table of Contents. Background Productivity Data Analysis by Application Domain Reducing the number of domains: Application Difficulty Topics for further discussion.

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Productivity Data Analysis and Issues

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  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

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