Parallel implementation of a bug report assignment recommender using deep learning

Abstract

For large software projects which receive many reports daily, assigning the most appropriate developer to fix a bug from a large pool of potential developers is both technically difficult and time-consuming. We introduce a parallel, highly scalable recommender system for bug report assignment. From a machine learning perspective, the core of such a system consists of a multi-class classification process using characteristics of a bug, like textual information and other categorical attributes, as features and the most appropriate developer as the predicted class. We use alternatively two Deep Learning classifiers: Convolutional and Recurrent Neural Networks. The implementation is realized on an Apache Spark engine, running on IBM Power8 servers. The experiments use real-world data from the Netbeans, Eclipse and Mozilla projects.