DigBug: Pre/post-processing operator selection for accurate bug localization

Bug localization is a recurrent maintenance task in software development. It aims at identifying relevant code locations (e.g., code files) that must be inspected to fix bugs. When such bugs are reported by users, the localization process become often overwhelming as it is mostly a manual task due t...

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Main Authors: KIM, Kisub, GHATPANDE, Sankalp, LIU, Kui, KOYUNCU, Anil, KIM, Dongsun, BISSYANDE, Tegawendé F., KLEIN, Jacques, LE TRAON, Yves
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Language:English
Published: Institutional Knowledge at Singapore Management University 2022
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Online Access:https://ink.library.smu.edu.sg/sis_research/7161
https://ink.library.smu.edu.sg/context/sis_research/article/8164/viewcontent/DigBug_Dig_into_Bug_JSS.pdf
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Institution: Singapore Management University
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spelling sg-smu-ink.sis_research-81642022-08-01T04:20:49Z DigBug: Pre/post-processing operator selection for accurate bug localization KIM, Kisub GHATPANDE, Sankalp LIU, Kui KOYUNCU, Anil KIM, Dongsun BISSYANDE, Tegawendé F. KLEIN, Jacques LE TRAON, Yves Bug localization is a recurrent maintenance task in software development. It aims at identifying relevant code locations (e.g., code files) that must be inspected to fix bugs. When such bugs are reported by users, the localization process become often overwhelming as it is mostly a manual task due to incomplete and informal information (written in natural languages) available in bug reports. The research community has then invested in automated approaches, notably using Information Retrieval techniques. Unfortunately, reported performance in the literature is still limited for practical usage. Our key observation, after empirically investigating a large dataset of bug reports as well as workflow and results of state-of-the-art approaches, is that most approaches attempt localization for every bug report without considering the different characteristics of the bug reports. We propose DigBug as a straightforward approach to specialized bug localization. This approach selects pre/post-processing operators based on the attributes of bug reports; and the bug localization model is parameterized in accordance as well. Our experiments confirm that departing from “one-size-fits-all” approaches, DigBug outperforms the state-of-the-art techniques by 6 and 14 percentage points, respectively in terms of MAP and MRR on average. 2022-07-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/7161 info:doi/10.1016/j.jss.2022.111300 https://ink.library.smu.edu.sg/context/sis_research/article/8164/viewcontent/DigBug_Dig_into_Bug_JSS.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 Bug characteristics Bug localization Bug report Fault localization Information retrieval Operator combination Software Engineering
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Bug characteristics
Bug localization
Bug report
Fault localization
Information retrieval
Operator combination
Software Engineering
spellingShingle Bug characteristics
Bug localization
Bug report
Fault localization
Information retrieval
Operator combination
Software Engineering
KIM, Kisub
GHATPANDE, Sankalp
LIU, Kui
KOYUNCU, Anil
KIM, Dongsun
BISSYANDE, Tegawendé F.
KLEIN, Jacques
LE TRAON, Yves
DigBug: Pre/post-processing operator selection for accurate bug localization
description Bug localization is a recurrent maintenance task in software development. It aims at identifying relevant code locations (e.g., code files) that must be inspected to fix bugs. When such bugs are reported by users, the localization process become often overwhelming as it is mostly a manual task due to incomplete and informal information (written in natural languages) available in bug reports. The research community has then invested in automated approaches, notably using Information Retrieval techniques. Unfortunately, reported performance in the literature is still limited for practical usage. Our key observation, after empirically investigating a large dataset of bug reports as well as workflow and results of state-of-the-art approaches, is that most approaches attempt localization for every bug report without considering the different characteristics of the bug reports. We propose DigBug as a straightforward approach to specialized bug localization. This approach selects pre/post-processing operators based on the attributes of bug reports; and the bug localization model is parameterized in accordance as well. Our experiments confirm that departing from “one-size-fits-all” approaches, DigBug outperforms the state-of-the-art techniques by 6 and 14 percentage points, respectively in terms of MAP and MRR on average.
format text
author KIM, Kisub
GHATPANDE, Sankalp
LIU, Kui
KOYUNCU, Anil
KIM, Dongsun
BISSYANDE, Tegawendé F.
KLEIN, Jacques
LE TRAON, Yves
author_facet KIM, Kisub
GHATPANDE, Sankalp
LIU, Kui
KOYUNCU, Anil
KIM, Dongsun
BISSYANDE, Tegawendé F.
KLEIN, Jacques
LE TRAON, Yves
author_sort KIM, Kisub
title DigBug: Pre/post-processing operator selection for accurate bug localization
title_short DigBug: Pre/post-processing operator selection for accurate bug localization
title_full DigBug: Pre/post-processing operator selection for accurate bug localization
title_fullStr DigBug: Pre/post-processing operator selection for accurate bug localization
title_full_unstemmed DigBug: Pre/post-processing operator selection for accurate bug localization
title_sort digbug: pre/post-processing operator selection for accurate bug localization
publisher Institutional Knowledge at Singapore Management University
publishDate 2022
url https://ink.library.smu.edu.sg/sis_research/7161
https://ink.library.smu.edu.sg/context/sis_research/article/8164/viewcontent/DigBug_Dig_into_Bug_JSS.pdf
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