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Presented Paper

  1. 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. 2. What Customers Want
  3. 3. What Requirements Tell Us
  4. 4. Standish Group [Standish94] <ul><li>Exceeded planned budget by 90% </li></ul><ul><li>Schedule by 222% </li></ul><ul><li>More than 50% of the projects had less than 50% requirements </li></ul>
  5. 5. Underlying Problems <ul><li>85% are at CMM 1 or 2 [CMU CMM95, Curtis93] </li></ul><ul><li>Scarcity of data </li></ul>
  6. 6. Consequences <ul><li>Early life-cycle estimates use a factor of 4 [Boehm81, Heemstra92] </li></ul>
  7. 7. Related Research: Economic Models
  8. 8. Why are Machine Learning algorithms not used more often for estimating early in the life cycle?
  9. 9. Related Research - 2
  10. 10. Goal <ul><li>Apply Machine Learning (Neural Network) </li></ul><ul><li>early in the software lifecycle </li></ul><ul><li>against Empirical Data </li></ul>
  11. 11. Neural Network
  12. 12. Data <ul><li>B2B Electronic Commerce Data </li></ul><ul><ul><li>Delphi-based </li></ul></ul><ul><ul><li>104 Vectors </li></ul></ul><ul><li>Fleet Management Software </li></ul><ul><ul><li>Delphi-based </li></ul></ul><ul><ul><li>433 Vectors </li></ul></ul>
  13. 13. Experiment 1: Product -Based Fleet to B2B
  14. 14. Experiment 1: Product Results
  15. 15. Experiment 2: Project -Based Results Fleet to B2B
  16. 16. Experiment 3: Product -Based B2B to Fleet
  17. 17. Extrapolation issue <ul><li>Largest SLOCs divided by each other </li></ul><ul><li>4398 / 2796 = 1.57 </li></ul>
  18. 18. Experiment 3: Product Results
  19. 19. Experiment 4: Project -Based Results B2B to Fleet
  20. 20. Results
  21. 21. Conclusions <ul><li>Bottom-up approach produced very good results on a project-basis </li></ul><ul><li>Results comparable between NN and stat. </li></ul><ul><li>Scaling helped </li></ul><ul><li>Estimation Approach is suitable for Prototype/Iterative Development </li></ul>
  22. 22. Future Directions <ul><li>Explore an extrapolation function </li></ul><ul><li>Apply other ML algorithms </li></ul><ul><li>Collect additional metrics </li></ul><ul><li>Integrate with COCOMO II </li></ul><ul><li>Conduct more experiments (additional data) </li></ul>

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