Title
Parallel Implementation of a Bug Report Assignment Recommender Using Deep Learning
Document Type
Conference Proceeding
Department or Administrative Unit
Computer Science
Publication Date
9-11-2017
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.
Recommended Citation
Florea, A.-C. ă. ă., Anvik, J., & Andonie, R. ă. (2017). Parallel Implementation of a Bug Report Assignment Recommender Using Deep Learning. Artificial Neural Networks and Machine Learning – ICANN 2017, 64–71. https://doi.org/10.1007/978-3-319-68612-7_8
Journal
ICANN 2017: Artificial Neural Networks and Machine Learning – ICANN 2017
Rights
© Springer International Publishing AG 2017
Comments
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.
Due to copyright restrictions, this article is not available for free download from ScholarWorks @ CWU.