Analyzing requirements and traceability information to improve bug localization

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 ac...

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Main Authors: RATH, Michael, LO, David, MADER, Patrick
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Language:English
Published: Institutional Knowledge at Singapore Management University 2018
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Online Access:https://ink.library.smu.edu.sg/sis_research/4290
https://ink.library.smu.edu.sg/context/sis_research/article/5293/viewcontent/p442_rath.pdf
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spelling sg-smu-ink.sis_research-52932019-02-21T08:39:35Z Analyzing requirements and traceability information to improve bug localization RATH, Michael LO, David MADER, Patrick 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). 2018-05-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/4290 info:doi/10.1145/3196398.3196415 https://ink.library.smu.edu.sg/context/sis_research/article/5293/viewcontent/p442_rath.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Computer Engineering Software Engineering
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Computer Engineering
Software Engineering
spellingShingle Computer Engineering
Software Engineering
RATH, Michael
LO, David
MADER, Patrick
Analyzing requirements and traceability information to improve bug localization
description 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).
format text
author RATH, Michael
LO, David
MADER, Patrick
author_facet RATH, Michael
LO, David
MADER, Patrick
author_sort RATH, Michael
title Analyzing requirements and traceability information to improve bug localization
title_short Analyzing requirements and traceability information to improve bug localization
title_full Analyzing requirements and traceability information to improve bug localization
title_fullStr Analyzing requirements and traceability information to improve bug localization
title_full_unstemmed Analyzing requirements and traceability information to improve bug localization
title_sort analyzing requirements and traceability information to improve bug localization
publisher Institutional Knowledge at Singapore Management University
publishDate 2018
url https://ink.library.smu.edu.sg/sis_research/4290
https://ink.library.smu.edu.sg/context/sis_research/article/5293/viewcontent/p442_rath.pdf
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