Concern localization using information retrieval: An empirical study on Linux kernel

Many software maintenance activities need to find code units (functions, files, etc.) that implement a certain concern (features, bugs, etc.). To facilitate such activities, many approaches have been proposed to automatically link code units with concerns described in natural languages, which are te...

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Main Authors: WANG, Shaowei, LO, David, XING, Zhenchang, JIANG, Lingxiao
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Language:English
Published: Institutional Knowledge at Singapore Management University 2011
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Online Access:https://ink.library.smu.edu.sg/sis_research/1396
https://ink.library.smu.edu.sg/context/sis_research/article/2395/viewcontent/ConcernLocalizationIR_2011.pdf
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spelling sg-smu-ink.sis_research-23952017-02-05T02:37:35Z Concern localization using information retrieval: An empirical study on Linux kernel WANG, Shaowei LO, David XING, Zhenchang JIANG, Lingxiao Many software maintenance activities need to find code units (functions, files, etc.) that implement a certain concern (features, bugs, etc.). To facilitate such activities, many approaches have been proposed to automatically link code units with concerns described in natural languages, which are termed as concern localization and often employ Information Retrieval (IR) techniques. There has not been a study that evaluates and compares the effectiveness of latest IR techniques on a large dataset. This study fills this gap by investigating ten IR techniques, some of which are new and have not been used for concern localization, on a Linux kernel dataset. The Linux kernel dataset contains more than 1,500 concerns that are linked to over 85,000 C functions. We have evaluated the effectiveness of the ten techniques on recovering the links between the concerns and the implementing functions and ranked the IR techniques based on their precisions on concern localization. 2011-10-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/1396 info:doi/10.1109/WCRE.2011.72 https://ink.library.smu.edu.sg/context/sis_research/article/2395/viewcontent/ConcernLocalizationIR_2011.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 concern localization information retrieval Linux kernel mean average precision Software Engineering
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic concern localization
information retrieval
Linux kernel
mean average precision
Software Engineering
spellingShingle concern localization
information retrieval
Linux kernel
mean average precision
Software Engineering
WANG, Shaowei
LO, David
XING, Zhenchang
JIANG, Lingxiao
Concern localization using information retrieval: An empirical study on Linux kernel
description Many software maintenance activities need to find code units (functions, files, etc.) that implement a certain concern (features, bugs, etc.). To facilitate such activities, many approaches have been proposed to automatically link code units with concerns described in natural languages, which are termed as concern localization and often employ Information Retrieval (IR) techniques. There has not been a study that evaluates and compares the effectiveness of latest IR techniques on a large dataset. This study fills this gap by investigating ten IR techniques, some of which are new and have not been used for concern localization, on a Linux kernel dataset. The Linux kernel dataset contains more than 1,500 concerns that are linked to over 85,000 C functions. We have evaluated the effectiveness of the ten techniques on recovering the links between the concerns and the implementing functions and ranked the IR techniques based on their precisions on concern localization.
format text
author WANG, Shaowei
LO, David
XING, Zhenchang
JIANG, Lingxiao
author_facet WANG, Shaowei
LO, David
XING, Zhenchang
JIANG, Lingxiao
author_sort WANG, Shaowei
title Concern localization using information retrieval: An empirical study on Linux kernel
title_short Concern localization using information retrieval: An empirical study on Linux kernel
title_full Concern localization using information retrieval: An empirical study on Linux kernel
title_fullStr Concern localization using information retrieval: An empirical study on Linux kernel
title_full_unstemmed Concern localization using information retrieval: An empirical study on Linux kernel
title_sort concern localization using information retrieval: an empirical study on linux kernel
publisher Institutional Knowledge at Singapore Management University
publishDate 2011
url https://ink.library.smu.edu.sg/sis_research/1396
https://ink.library.smu.edu.sg/context/sis_research/article/2395/viewcontent/ConcernLocalizationIR_2011.pdf
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