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Using Machine Learning to Predict Project Effort: Empirical Case Studies in Data-starved Domains

Using Machine Learning to Predict Project Effort: Empirical Case Studies in Data-starved Domains. Gary D. Boetticher Department of Software Engineering University of Houston - Clear Lake. What Customers Want. What Requirements Tell Us. Standish Group [Standish94].

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Using Machine Learning to Predict Project Effort: Empirical Case Studies in Data-starved Domains

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  1. Using Machine Learning to Predict Project Effort: Empirical Case Studies in Data-starved Domains Gary D. Boetticher Department of Software Engineering University of Houston - Clear Lake

  2. What Customers Want

  3. What Requirements Tell Us

  4. Standish Group [Standish94] • Exceeded planned budget by 90% • Schedule by 222% • More than 50% of the projects had less than 50% requirements

  5. Underlying Problems 85% are at CMM 1 or 2 [CMU CMM95, Curtis93] Scarcity of data

  6. Consequences Early life-cycle estimates use a factor of 4 [Boehm81, Heemstra92]

  7. Related Research: Economic Models

  8. Why are Machine Learning algorithms not used more often for estimating early in the life cycle?

  9. Related Research - 2

  10. Goal Apply Machine Learning (Neural Network) early in the software lifecycle against Empirical Data

  11. Neural Network

  12. Data • B2B Electronic Commerce Data • Delphi-based • 104 Vectors • Fleet Management Software • Delphi-based • 433 Vectors

  13. Experiment 1: Product-Based Fleet to B2B

  14. Experiment 1: Product Results

  15. Experiment 2: Project-Based Results Fleet to B2B

  16. Experiment 3: Product-Based B2B to Fleet

  17. Extrapolation issue Largest SLOCs divided by each other 4398 / 2796 = 1.57

  18. Experiment 3: Product Results

  19. Experiment 4: Project-Based Results B2B to Fleet

  20. Results

  21. Conclusions • Bottom-up approach produced very good results on a project-basis • Results comparable between NN and stat. • Scaling helped • Estimation Approach is suitable for Prototype/Iterative Development

  22. Future Directions • Explore an extrapolation function • Apply other ML algorithms • Collect additional metrics • Integrate with COCOMO II • Conduct more experiments (additional data)

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