Bug Triaging Based on Tossing Sequence Modeling

• Published in: Journal of computer science and technology Vol. 34; no. 5; pp. 942 - 956
• Main Authors: Xi, Sheng-Qu, Yao, Yuan, Xiao, Xu-Sheng, Xu, Feng, Lv, Jian
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.09.2019; Springer; Springer Nature B.V
• Subjects: Analysis; Artificial Intelligence; Computer Science; Data Structures and Information Theory; Debugging; Information retrieval; Information Systems Applications (incl.Internet); Machine learning; Metadata; Regular Paper; Software development; Software Engineering; Theory of Computation; software repository minings; bug triaging; tossing sequence
• ISSN: 1000-9000; 1860-4749
• DOI: 10.1007/s11390-019-1953-5

Bug triaging, which routes the bug reports to potential fixers, is an integral step in software development and maintenance. To make bug triaging more efficient, many researchers propose to adopt machine learning and information retrieval techniques to identify some suitable fixers for a given bug report. However, none of the existing proposals simultaneously take into account the following three aspects that matter for the efficiency of bug triaging: 1) the textual content in the bug reports, 2) the metadata in the bug reports, and 3) the tossing sequence of the bug reports. To simultaneously make use of the above three aspects, we propose i T riage which first adopts a sequence-to-sequence model to jointly learn the features of textual content and tossing sequence, and then uses a classification model to integrate the features from textual content, metadata, and tossing sequence. Evaluation results on three different open-source projects show that the proposed approach has significantly improved the accuracy of bug triaging compared with the state-of-the-art approaches.