Automatic Classification of Software Change Request Using Multi-label Machine Learning Methods

Abstract

Automatic text classification of the software change request (CR) can be used for automating impact analysis, bug triage and effort estimation. In this paper, we focus on the automation of the process for assigning CRs to developers and present a solution that is based on automatic text classification of CRs. In addition our approach provides the list of source files, which are required to be modified and an estimate for the time required to resolve a given CR. To perform experiments, we downloaded the set of resolved CRs from the OSS project's repository for Mozilla. We labeled each CR with multiple labels i.e., the developer name, the list of source files, and the time spent to resolve the CR. To train the classifier, our approach applies the Problem Transformation and Algorithm Adaptation methods of multi-label machine learning to the multi-labeled CR data. With this approach, we have obtained precision levels up to 71.3% with 40.1% recall.