Smarter software engineering: Practical data mining approaches

Proceedings - 27th Annual NASA Goddard Software Engineering Workshop, SEW 2002

Volumn , Issue , 2002, Pages 1-38

  Menzies, T ^a   Boetticher, G D ^b  

^a WEST VIRGINIA UNIVERSITY   (United States)

^b UHCL ^*  (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; DECISION TREES; GENETIC ALGORITHMS; INDUCTIVE LOGIC PROGRAMMING (ILP); NASA; SOFTWARE ENGINEERING; TECHNICAL PRESENTATIONS; TREES (MATHEMATICS);

BAYESIAN; PROJECT PLANNING;

DATA MINING;

EID: 9744280275     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/NASASE.2002.1176229     Document Type: Conference Paper

References (34)

1. Agrawal, R., and T.Imeilinski and A.Swami "Mining Association Rules between Sets of Items in Large Databases," Proceedings of the 1993 ACM SIGMOD Conference, Washington DC, USA.
2. Bergadano, F., and D.Gunetti, Inductive Logic Programming: From Machine Learning to Software Engineering, The MIT Press, 1995.
3. Berry, M. J. A., and G., Linoff, Data Mining For Marketing, Sales, and Customer Support, John Wiley & Sons, Inc., New York, 1997.
4. Boetticher, G., "An Assessment of Metric Contribution in the Construction of a Neural Network-Based Effort Estimator," Second International Workshop on Soft Computing Applied to Software Engineering, Enschade, NL, 2001. Available from: http://nas.cl.uh.edu/boetticher/publications.html
5. Boetticher, G., "Using Machine Learning to Predict Project Effort: Empirical Case Studies in Data-Starved Domains," First International Workshop on Model-based Requirements Engineering, San Diego, 2001. Available from:http://nas.cl.uh.edu/boetticher/publications.html
6. Bradley, P., U. Fayyad, and C. Reina. "Scaling clustering algorithms to large databases". In KDD'98.
7. Bratko, I., I. Mozetic, and N. Lavrac, KARDIO: a Study in Deep and Qualitative Knowledge for Expert Systems, MIT Press, 1989.
8. Breiman, L., J. Friedman, R. Olshen, C. Stone, "Classification and Regression Trees," Wadsworth International Group, 1984.
9. Burgess, C.J., and Martin Lefley. "Can genetic programming improve software effort estimation? A comparative evaluation," Information and Software Technology, 43(14):863-873, December 2001.
10. P. Cheeseman, D. Freeman, J. Kelly, M. Self, J. Stutz, and W. Taylor. "Autoclass: a bayesian classification system," In Proceedings of the Fifth International Conference on Machine Learning, Morgan Kaufman, 1988.
11. S.Chulani, B. Boehm, and B. Steece "Bayesian analysis of empirical software engineering cost models," IEEE Transaction on Software Engineering, 25(4), July/August 1999.
12. W. W. Cohen, "Inductive specification recovery: Understanding software by learning from example behaviors," Automated Software Engineering, 2:107-129, 1995.
13. DeJong, K.A., and Spears, W.M. "An Analysis of the Interacting Roles of Population Size and Crossover in Genetic Algorithms," Proc. First Workshop Parallel Problem Solving from Nature, Springer-Verlag, Berlin, 1990.
14. Dietterich, T. G., "Machine Learning Research: Four Current Directions," AI Magazine, 18 (4), 1997, Pp. 97-136. Available from: ftp://ftp.cs.orst.edu/pub/tgd/papers/aimag-survey.ps.gz
15. Feather, M.S., and T. Menzies: "Converging on the Optimal Attainment of Requirements," IEEE Joint Conference On Requirements Engineering ICRE'02 and RE'02, 9-13th September, University of Essen, Germany, 2002. Available from: http://tim.menzies.com/pdf/02re02.pdf
16. Fenton, N., and M. Neil "Software Metrics: A Roadmap," International Conference on Software Engineering, 2000. Available from http://www.dcs.qmul.ac.uk/~norman/papers/metrics-roadmap.pdf
17. Goldberg, D.E., Genetic Algorithms in Search, Optimization, and Machine Learning, Addison-Wesley, Reading, Massachusetts, 1989
18. Khoshgoftaar, T.M., and E.B. Allen. "Model software quality with classification trees," in H. Pham, editor, "Recent Advances in Reliability and Quality Engineering", World Scientific, 1999.
19. Mendonca, M., and N.L. Sunderhaft, "Mining Software Engineering Data: A Survey," A DACS State-of-the-Art Report, September 1999. Available from http://www.dacs.dtic.mil/techs/datamining/
20. Menzies, T., "Practical Machine Learning for Software Engineering and Knowledge Engineering," Handbook of Software Engineering and Knowledge Engineering, (volume 1, 2001). Available from http://tim.menzies.com/pdf/00ml.pdf
21. Menzies, T., and Y. Hu, "Reusing models for requirements engineering," First International Workshop on Model-based Requirements Engineering, 2001. Available from http://tim.menzies.com/pdf/01reusere.pdf
22. Menzies, T., and Y. Hu, "Constraining discussions in requirements engineering," First International Workshop on Model-based Requirements Engineering, San Diego, 2001. Available from http://tim.menzies.com/pdf/01lesstalk.pdf
23. Menzies. T., and J. Kiper, "Better reasoning about software engineering activities," Automated Software Engineering, 2001. Available from http://tim.menzies.com/pdf/01ml4re.pdf
24. Michalski, R.S., "Toward a unified theory of learning," In B.G. Buchanan and D.C. Wilkins, editors, "Reading in Knowledge Acquisition and Learning", pages 7-38. Morgan Kaufmann, 1993.
25. Mitchell, T., Machine Learning, McGraw-Hill, 1997.
26. Morasca, S., and Gunther Ruhe, Guest editors' introduction of the Special issue on "Knowledge Discovery from Software Engineering Data," International Journal of Software Engineering and Knowledge Engineering, October, 1999.
27. Pearce, D., "The induction of fault diagnosis systems from qualitative models," in Proc. AAAI-88, 1988.
28. Poole, D. L., A. K. Mackworth, and R. G. Goebel., Computational Intelligence: A Logical Approach, Oxford University Press, New York, 1998.
29. Porter, A.A., and R.W. Selby "Empirically guided software development using metric-based classification trees," IEEE Software, Pp. 46-54, March 1990.
30. Quinlan, R., C4.5: Programs for Machine Learning, Morgan Kaufman, 1992.
31. Srinivasan, K., and D. Fisher, "Machine learning approaches to estimating software development effort," IEEE Transactions on Software Engineering, Pp. 126-137, February 1995.
32. Tian, J., and M.V. Zelkowitz "Complexity measure evaluation and selection," IEEE Transactions on Software Engineering, 21(8), Pp. 641-649, August 1995.
33. Webb, G., "Efficient search for association rules," Proceeding of KDD-2000 Boston, MA, 2000.
34. Zhang, Du, "Applying Machine Learning Algorithms in Software Development," Modelling Software System Structures in a fastly moving scenario, Santa Margherita Ligure, Italy, 2000.