JISBD 04: Finding Defective Software Modules by Means of Data Mining Techniques

The characterization of defective modules in software engineering remains a challenge. In this work, we use data mining techniques to search for rules that indicate modules with a high probability of being defective. Using datasets from the PROMISE repository 1, we first applied feature selection to...

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Bibliographic Details
Published in:Revista IEEE América Latina 2009-06, Vol.7 (3), p.377
Main Authors: Riquelme, J.C, Ruiz, R, Rodriguez, D, Aguilar-Ruiz, J.S
Format: Article
Language:English
Subjects:
Algorithms
Computer programs
Data mining
Modules
Repositories
Searching
Software
Subgroups
Online Access:Get full text
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Summary:The characterization of defective modules in software engineering remains a challenge. In this work, we use data mining techniques to search for rules that indicate modules with a high probability of being defective. Using datasets from the PROMISE repository 1, we first applied feature selection to work only with those attributes from the datasets capable of predicting defective modules. Then, a genetic algorithm search for rules characterising subgroups with a high probability of being defective. This algorithm overcomes the problem of unbalanced datasets where the number of non-defective samples in the dataset highly outnumbers the defective ones.
ISSN:1548-0992
1548-0992
DOI:10.1109/TLA.2009.5336637