Parallel Implementation of a Bug Report Assignment Recommender Using Deep Learning

Document Type

Conference Proceeding

Department or Administrative Unit

Computer Science

Publication Date



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.


This article was originally published in ICANN 2017: Artificial Neural Networks and Machine Learning – ICANN 2017. The full-text article from the publisher can be found here.

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ICANN 2017: Artificial Neural Networks and Machine Learning – ICANN 2017


© Springer International Publishing AG 2017