1 / 16

Analysis of Process Maturity and Productivity with SRDR Data

Analysis of Process Maturity and Productivity with SRDR Data. USC CSSE Annual Research Review April 29 – May 1, 2014 Anandi Hira, Jo Ann Lane. Outline. Motivation Explanation of the SRDR Data Repository Data Processing Analysis Procedure Results of Analyses per Taxonomy and Comparison

havyn
Télécharger la présentation

Analysis of Process Maturity and Productivity with SRDR Data

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Analysis of Process Maturity and Productivity with SRDR Data USC CSSE Annual Research Review April 29 – May 1, 2014 Anandi Hira, Jo Ann Lane

  2. Outline • Motivation • Explanation of the SRDR Data Repository • Data Processing • Analysis Procedure • Results of Analyses per Taxonomy and Comparison • COCOMO IIComparisons • Application Domains • Productivity Types • Factors to Consider in Productivity Analysis -> Future Work • Parameter Suggestions for Future Research and Analyses • Questions/Suggestions

  3. Motivation • Improve Productivity • Invest resources to improve processes • Process Certification Productivity ? • SRDR Data

  4. SRDR Data • Relevant Parameters provided: • Total Effort (hours) • Equivalent Total SLOC • SLOC Counting Method • CMM/CMMI Levels • Unused Parameters • Effort distribution per phase, Programming language(s), Personnel Experience • Software Resources Data Reporting (SRDR) • Quantitative data and associated parametric project characteristics • DoD software-intensive system development projects • Data analysis and trends research

  5. Data Processing • Normalizing Data • Logical SLOC • Counting adjustment factors • Non-comment: 0.66 * SLOC • Physical: 0.34 * SLOC • Filtering Data • Remove outliers • Remove points without relevant parameters • Projects < 10 EKSLOC • Levels 2 and 4

  6. Analysis Procedure

  7. COCOMO Comparisons

  8. Application Domains

  9. Application Domains – ANOVA Test Results

  10. Productivity Types

  11. Productivity Types – ANOVA Test Results

  12. Findings and Conclusions • COCOMOComparisons • Data broken up by sizes do not closely correspond to parameter ratings • Average of all data corresponds to parameter rating • Application Domains and Productivity Types • Inconsistence with regards to productivity increase/decrease from Level 3 to Level 5 • Difference in productivity ranges of Level 3 and 5 are statistically insignificant

  13. Future Work – Factors to Consider • Cost drivers and parameters that effect productivity not provided and random with respect to time • Staff experience • Tool support • Code reuse • Improved architecting and risk resolution • Counting methods not standard and may skew analysis • Code reuse gains factored and normalized in data • Analysis of trends of productivity over time • IDPD

  14. Productivity Over Time

  15. Future Work – Parameter Suggestions • (Relative) Time of Project Implementation • Other data points • Adopting process maturity levels • Equivalent Metric for Non-Development Effort • Equivalent Output Metric per Phase/Activity • Rework SLOC and Effort • Volatility • Complexity

  16. Questions and Suggestions

More Related