Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

5-2018

Abstract

Locating bugs in industry-size software systems is time consuming and challenging. An automated approach for assisting the process of tracing from bug descriptions to relevant source code benefits developers. A large body of previous work aims to address this problem and demonstrates considerable achievements. Most existing approaches focus on the key challenge of improving techniques based on textual similarity to identify relevant files. However, there exists a lexical gap between the natural language used to formulate bug reports and the formal source code and its comments. To bridge this gap, state-of-the-art approaches contain a component for analyzing bug history information to increase retrieval performance. In this paper, we propose a novel approach TraceScore that also utilizes projects' requirements information and explicit dependency trace links to further close the gap in order to relate a new bug report to defective source code files. Our evaluation on more than 13,000 bug reports shows, that TraceScore significantly outperforms two state-of-the-art methods. Further, by integrating TraceScore into an existing bug localization algorithm, we found that TraceScore significantly improves retrieval performance by 49% in terms of mean average precision (MAP).

Discipline

Computer Engineering | Software Engineering

Research Areas

Data Science and Engineering

Publication

Proceedings of the 15th International Conference on Mining Software Repositories (MSR 2018), Gothenburg, Sweden, 2018 May 28-29

First Page

442

Last Page

453

ISBN

9781450357166

Identifier

10.1145/3196398.3196415

Publisher

ACM, New York, USA

City or Country

Gothenburg, Sweden

Additional URL

https://doi.org/10.1145/3196398.3196415

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