Attribute Selection in Software Engineering Datasets for Detecting Fault Modules

Abstract

Decision making has been traditionally based on managers experience. At present, there is a number of software engineering (SE) repositories, and furthermore, automated data collection tools allow managers to collect large amounts of information, not without associated problems. On the one hand, such a large amount of information can overload project managers. On the other hand, problems found in generic project databases, where the data is collected from different organizations, is the large disparity of its instances. In this paper, we characterize several software engineering databases selecting attributes with the final aim that project managers can have a better global vision of the data they manage. In this paper, we make use of different data mining algorithms to select attributes from the different datasets publicly available (PROMISE repository), and then, use different classifiers to defect faulty modules. The results show that in general, the smaller datasets maintain the prediction capability with a lower number of attributes than the original datasets.