Data collection for Software Defect Prediction - An exploratory case study of open source software projects

Abstract

Software Defect Prediction (SDP) empirical studies are highly biased with the quality of data and widely suffer from limited generalizations. The main reasons are the lack of data and its systematic data collection procedures. Our research aims at producing the first systematically defined data collection procedure for SDP datasets that are obtained by linking separate development repositories. This paper is the first step to achieving that objective, performing an exploratory study. We review the existing literature on approaches and tools used in the collection of SDP datasets, derive a detailed collection procedure and test it in this exploratory study. We quantify the bias that may be caused by the issues we identified and we review 35 tools for software product metrics collection. The most critical issues are many-to-many relation between bug-file links, duplicated bug-file links and the issue of untraceable bugs. Our research provides more detailed, experience based data collection procedure, crucial for further development of SDP body of knowledge. Furthermore, our findings enabled us to develop the automatic data collection tool.