Applying Convolutional Neural Networks With Different Word Representation Techniques to Recommend Bug Fixers

• Published in: IEEE access 2020, Vol.8, p.213729-213747
• Main Authors: Zaidi, Syed Farhan Alam, Awan, Faraz Malik, Lee, Minsoo, Woo, Honguk, Lee, Chan-Gun
• Format: Article
• Language: English
• Subjects: Artificial neural networks; bug fixing; bug report; Bug triage; CNN; Computer bugs; Datasets; Deep learning; ELMo; Feature extraction; GloVe; Machine learning; Neural networks; Open source software; recommending bug fixer; Recurrent neural networks; Representations; Source code; Support vector machines; Task analysis; word embedding; word representation; Word2Vec; Words (language)
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2020.3040065

Bug triage processes are intended to assign bug reports to appropriate developers effectively, but they typically become bottlenecks in the development process-especially for large-scale software projects. Recently, several machine learning approaches, including deep learning-based approaches, have been proposed to recommend an appropriate developer automatically by learning past assignment patterns. In this paper, we propose a deep learning-based bug triage technique using a convolutional neural network (CNN) with three different word representation techniques: Word to Vector (Word2Vec), Global Vector (GloVe), and Embeddings from Language Models (ELMo). Experiments were performed on datasets from well-known large-scale open-source projects, such as Eclipse and Mozilla, and top-k accuracy was measured as an evaluation metric. The experimental results suggest that the ELMo-based CNN approach performs best for the bug triage problem. GloVe-based CNN slightly outperforms Word2Vec-based CNN in many cases. Word2Vec-based CNN outperforms GloVe-based CNN when the number of samples per class in the dataset is high enough.