Searching for Rules to find Defective Modules in Unbalanced Data Sets

Abstract

The characterisation 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 data sets from the PROMISE repository, we first applied feature selection (attribute selection) to work only with those attributes from the data sets capable of predicting defective modules. With the reduced data set, a genetic algorithm is used to search for rules characterising modules with a high probability of being defective. This algorithm overcomes the problem of unbalanced data sets where the number of non defective samples in the data set highly outnumbers the defective ones