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)