JISBD 04: Identificación de fallos en módulos software utilizando técnicas de minería de datos (JISBD 04: Finding Defective Software Modules by Means of Data Mining Techniques)

Jose C Riquelme (riquelme@lsi.us.es)1, Roberto Ruiz (robertoruiz@upo.es)2, Daniel Rodriguez (daniel.rodriguezg@uah.es)2, Jesus Jesus S Aguilar Ruiz (aguilar@upo.es)2

1Universidad de Sevilla
2Universidad Pablo de Olavide

This paper appears in: Revista IEEE América Latina

Publication Date: July 2009
Volume: 7,   Issue: 3 
ISSN: 1548-0992

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 repository1, 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

Index Terms:
Defect detection and defect prediction in software modules, data mining   

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