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Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/1185

Title: Software Defect Association Mining and Defect Correction Effort Prediction
Authors: Song, Q
Shepperd, MJ
Cartwright, MH
Mair, C
Keywords: Empirical software engineering
Defects
Prediction
Association rules
Machine learning
Data mining
Publication Date: 2006
Publisher: IEEE Computer Society
Citation: IEEE Transactions on Software Engineering, 32(2): 69 - 82, Feb 2006
Abstract: Much current software defect prediction work concentrates on the number of defects remaining in software system. In this paper, we present association rule mining based methods to predict defect associations and defect-correction effort. This is to help developers detect software defects and assist project managers in allocating testing resources more effectively. We applied the proposed methods to the SEL defect data consisting of more than 200 projects over more than 15 years. The results show that for the defect association prediction, the accuracy is very high and the false negative rate is very low. Likewise for the defect-correction effort prediction, the accuracy for both defect isolation effort prediction and defect correction effort prediction are also high. We compared the defect-correction effort prediction method with other types of methods: PART, C4.5, and Na¨ıve Bayes and show that accuracy has been improved by at least 23%. We also evaluated the impact of support and confidence levels on prediction accuracy, false negative rate, false positive rate, and the number of rules. We found that higher support and confidence levels may not result in higher prediction accuracy, and a sufficient number of rules is a precondition for high prediction accuracy.
URI: http://bura.brunel.ac.uk/handle/2438/1185
DOI: http://dx.doi.org/10.1109/TSE.2006.1599417
ISSN: 0098-5589
Appears in Collections:B-SERC Research Papers
School of Information Systems, Computing and Mathematics Research Papers
Computer Science

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