Combining Word Embedding with Information Retrieval to Recommend Similar Bug Reports

Similar bugs are bugs that require handling of many common code files. Developers can often fix similar bugs with a shorter time and a higher quality since they can focus on fewer code files. Therefore, similar bug recommendation is a meaningful task which can improve development efficiency. Rocha e...

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Bibliographic Details
Published in2016 IEEE 27th International Symposium on Software Reliability Engineering (ISSRE) pp. 127 - 137
Main Authors Xinli Yang, Lo, David, Xin Xia, Lingfeng Bao, Jianling Sun
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.10.2016
Subjects
Computer bugs
Context
Context modeling
Data mining
Information retrieval
Recommendation Systems
Similar Bugs
Software engineering
Word Embedding
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Summary:Similar bugs are bugs that require handling of many common code files. Developers can often fix similar bugs with a shorter time and a higher quality since they can focus on fewer code files. Therefore, similar bug recommendation is a meaningful task which can improve development efficiency. Rocha et al. propose the first similar bug recommendation system named NextBug. Although NextBug performs better than a start-of-the-art duplicated bug detection technique REP, its performance is not optimal and thus more work is needed to improve its effectiveness. Technically, it is also rather simple as it relies only upon a standard information retrieval technique, i.e., cosine similarity. In the paper, we propose a novel approach to recommend similar bugs. The approach combines a traditional information retrieval technique and a word embedding technique, and takes bug titles and descriptions as well as bug product and component information into consideration. To evaluate the approach, we use datasets from two popular open-source projects, i.e., Eclipse and Mozilla, each of which contains bug reports whose bug ids range from [1,400000]. The results show that our approach improves the performance of NextBug statistically significantly and substantially for both projects.
ISSN:2332-6549
DOI:10.1109/ISSRE.2016.33