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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 |
Issue 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: | Computer Science Dept of Computer Science Research Papers Software Engineering (B-SERC) |
Files in This Item:
File | Description | Size | Format | |
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Software Defect 2006.pdf | 4.24 MB | Adobe PDF | View/Open |
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