An investigation on the use of machine learned models for estimating software correctability

International Journal of Software Engineering and Knowledge Engineering

Volumn 9, Issue 5, 1999, Pages 565-593

  De Almeida, Mauricio A ^a   Lounis, Hakim ^b   Melo, Walcelio L ^c  

^a Laboratory of Integrated Systems   (Brazil)

^b Ctr de Rech Info de Montreal   (Canada)

^c UNIVERSIDADE CATÓLICA DE BRASÍLIA   (Brazil)

Author keywords

Machine learning algorithms; Predictive software quality model building; Software correctability

Indexed keywords

EID: 2642557078     PISSN: 02181940     EISSN: None     Source Type: Journal    
DOI: 10.1142/S0218194099000310     Document Type: Article

References (31)

1. A. Porter and R. Selby, "Empirically-guided software development using metric-based classification trees", IEEE Software 7, 2 (1990) 46-54.
2. V. Basili, Condon, K. El Emam, R. B. Hendrick and W. L. Melo, "Characterizing and modeling the cost of rework in a library of reusable software somponents", in Proc. IEEE 19th Int'l. Conf. on Software Eng., Boston, MA, May 1997.
3. M. Jorgensen, "Experience with the accuracy of software maintenance task effort prediction models", in IEEE TSE 21, 8 (1995) 674-681.
4. L. Briand, W. M. Thomas and C. J. Hetmanski, "Modeling and managing risk early in software development", in Proc. 15th Int'l Conf. on Software Engineering, 1993. pp. 55-65
5. L. Briand, V. Basili and C. Hetmanski, "A pattern recognition approach for software engineering data analysis", in IEEE TSE 18, 1 (1992).
6. D. Hosmer and S. Lemeshow, Applied Logistic Regression (Wiley-Interscience, 1989).
7. J. R. Quinlan, C4.5: Programs for Machine Learning (Morgan Kaufmann, 1993).
8. Amadeus Software Research Inc., Getting Started with Amadeus, Amadeus Measurement System, 1994.
9. Y. Mao, H. A. Sahraoui and H. Lounis, "Reusability hypothesis verification using machine learning techniques: A case study", in Proc. 13th IEEE Int. Automated Software Engineering Conference, 1998, pp. 84-93.
10. R. Boswell, "Manual for NewID", The Turing Institute, January 1990.
11. P. Clark and T. Niblett, "The CN2 induction algorithm", in Machine Learning Journal 3, 4 (1989) 261-283.
12. J. R. Quinlan, "Learning logical definitions from relations", in Machine Learning Journal 5, 3 (1990) 239-266.
13. J. R. Quinlan, "Discovering rules from large collections of examples: a case study", in E.S in the Micro-Electronic Age, ed. D. Michie (Edinburgh University Press, 1979).
14. J. R. Quinlan, "Induction of decision trees", Machine Learning Journal 1, 1 (1986) 81-106.
15. L. Breiman, J. Friedman, R. Olshen and C. Stone, Classification and Regression Trees (Wadsworth, 1984).
16. B. Cestnik, I. Bratko and I. Kononenko, "ASSISTANT 86: a knowledge elicitation tool for sophisticated users", Progress in Machine Learning (Sigma Press, 1987).
17. R. S. Michalski, I. Mozetic, J. Hong and N. Lavrac, "The AQ15 inductive learning system: an overview and experiments", Technical report UIUCDCS-R-86-1260, Deparment of Computer Science, University of Illinois at Urbana-Champaign.
18. H. Lounis and G. Bisson, "Evaluation of learning systems: an artificial data-based approach", in Lecture Notes in Artificial Intelligence, March 1991, pp. 463-481.
19. G. Heller, J. Valett and M. Wild, "Data Collection Procedure for the Software Engineering Laboratory (SEL) Database", Technical Report SEL-92-002, Software Engineering Laboratory, 1992.
20. D. Doubleday, "ASAP: An Ada Static Source Code Analyzer Program", CS-TR-1895, Computer Science Department, University of Maryland, College Park, August 1995.
21. H. Lounis and W. L. Melo, "Identifying and measuring coupling on modular systems", in Proc. 7th Int'l Conf. on Software Technology, organized by Centro Internacional de Tecnologia do Software (CTIS), Curitiba, Parana, Brazil, pp. 23-40.
22. S. Benlarbi and W. L. Melo, "Polymorphism measures for early risk prediction", in Proc. 21st Int. Conf. on Software Engineering (ICSE) (IEEE Press, 1999).
23. L. Briand, J. Wüst, S. Ikonomovski and H. Lounis, "Investigating quality factors in object-oriented designs: an industrial case study", in Proc. 21st Int. Conf. on Software Engineering (ICSE) (IEEE Press, 1999).
24. L. Briand, S. Morasca and V. Basili, "Defining and Validating High-Level Design Metrics", Technical Report, University of Maryland, Department of Computer Science, College Park, MD, 20742, 1994, CS-TR-3301.
25. W. W. Cohen and P. Devanbu, "A Comparative Study of Inductive Logic Programming Methods for Software Fault Prediction", Technical Report AT&T Labs-Research, 1996.
26. F. Lanubile and G. Visaggio, "Evaluating predictive quality models derived from software measures: lessons learned", Journal of Systems and Software 38 (1997) 225-234.
27. V. Basili and B. Perricone, "Software errors and complexity: an empirical investigation", in CACM 27, 1 (1984) 42-52.
28. S. M. Weiss and C. A. Kulikowski, Computer Systems That Learn (Morgan Kaufmann, 1991).
29. J. Munson and K. Khoshgoftaar, "The detection of fault-prone programs", IEEE TSE SE-18, 5 (1992) 423-433.
30. Dunteman, Principal Component Analysis (Sage, 1989).
31. S. Menard, Applied Logistic Regression Analysis (Sage, 1995).