Pieces of contextual information suitable for predicting co-changes? An empirical study

Models that predict software artifact co-changes have been proposed to assist developers in altering a software system and they often rely on coupling. However, developers have not yet widely adopted these approaches, presumably because of the high number of false recommendations. In this work, we c...

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Main Authors: WIESE, Igor Scaliante, KURODA, Rodrigo Takashi, STEINMACHER, Igor, OLIVA, Gustavo A., RÉ, Reginaldo, TREUDE, Christoph, GEROSA, Marco Aurélio
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Language:English
Published: Institutional Knowledge at Singapore Management University 2019
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Online Access:https://ink.library.smu.edu.sg/sis_research/8795
https://ink.library.smu.edu.sg/context/sis_research/article/9798/viewcontent/sqj19.pdf
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spelling sg-smu-ink.sis_research-97982024-05-30T08:48:09Z Pieces of contextual information suitable for predicting co-changes? An empirical study WIESE, Igor Scaliante KURODA, Rodrigo Takashi STEINMACHER, Igor OLIVA, Gustavo A. RÉ, Reginaldo TREUDE, Christoph GEROSA, Marco Aurélio Models that predict software artifact co-changes have been proposed to assist developers in altering a software system and they often rely on coupling. However, developers have not yet widely adopted these approaches, presumably because of the high number of false recommendations. In this work, we conjecture that the contextual information related to software changes, which is collected from issues (e.g., issue type and reporter), developers’ communication (e.g., number of issue comments, issue discussants and words in the discussion), and commit metadata (e.g., number of lines added, removed, and modified), improves the accuracy of co-change prediction. We built customized prediction models for each co-change and evaluated the approach on 129 releases from a curated set of 10 Apache Software Foundation projects. Comparing our approach with the widely used association rules as a baseline, we found that contextual information models and association rules provide a similar number of cochange recommendations, but our models achieved a significantly higher F-measure. In particular, we found that contextual information significantly reduces the number of false recommendations compared to the baseline model. We conclude that contextual information is an important source for supporting change prediction and may be used to warn developers when they are about to miss relevant artifacts while performing a software change. 2019-12-01T08:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/8795 info:doi/10.1007/s11219-019-09456-3 https://ink.library.smu.edu.sg/context/sis_research/article/9798/viewcontent/sqj19.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 Co-change prediction Logical coupling Change coupling Change propagation Change impact analysis Social factors Contextual information Software Engineering
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Co-change prediction
Logical coupling
Change coupling
Change propagation
Change impact analysis
Social factors
Contextual information
Software Engineering
spellingShingle Co-change prediction
Logical coupling
Change coupling
Change propagation
Change impact analysis
Social factors
Contextual information
Software Engineering
WIESE, Igor Scaliante
KURODA, Rodrigo Takashi
STEINMACHER, Igor
OLIVA, Gustavo A.
RÉ, Reginaldo
TREUDE, Christoph
GEROSA, Marco Aurélio
Pieces of contextual information suitable for predicting co-changes? An empirical study
description Models that predict software artifact co-changes have been proposed to assist developers in altering a software system and they often rely on coupling. However, developers have not yet widely adopted these approaches, presumably because of the high number of false recommendations. In this work, we conjecture that the contextual information related to software changes, which is collected from issues (e.g., issue type and reporter), developers’ communication (e.g., number of issue comments, issue discussants and words in the discussion), and commit metadata (e.g., number of lines added, removed, and modified), improves the accuracy of co-change prediction. We built customized prediction models for each co-change and evaluated the approach on 129 releases from a curated set of 10 Apache Software Foundation projects. Comparing our approach with the widely used association rules as a baseline, we found that contextual information models and association rules provide a similar number of cochange recommendations, but our models achieved a significantly higher F-measure. In particular, we found that contextual information significantly reduces the number of false recommendations compared to the baseline model. We conclude that contextual information is an important source for supporting change prediction and may be used to warn developers when they are about to miss relevant artifacts while performing a software change.
format text
author WIESE, Igor Scaliante
KURODA, Rodrigo Takashi
STEINMACHER, Igor
OLIVA, Gustavo A.
RÉ, Reginaldo
TREUDE, Christoph
GEROSA, Marco Aurélio
author_facet WIESE, Igor Scaliante
KURODA, Rodrigo Takashi
STEINMACHER, Igor
OLIVA, Gustavo A.
RÉ, Reginaldo
TREUDE, Christoph
GEROSA, Marco Aurélio
author_sort WIESE, Igor Scaliante
title Pieces of contextual information suitable for predicting co-changes? An empirical study
title_short Pieces of contextual information suitable for predicting co-changes? An empirical study
title_full Pieces of contextual information suitable for predicting co-changes? An empirical study
title_fullStr Pieces of contextual information suitable for predicting co-changes? An empirical study
title_full_unstemmed Pieces of contextual information suitable for predicting co-changes? An empirical study
title_sort pieces of contextual information suitable for predicting co-changes? an empirical study
publisher Institutional Knowledge at Singapore Management University
publishDate 2019
url https://ink.library.smu.edu.sg/sis_research/8795
https://ink.library.smu.edu.sg/context/sis_research/article/9798/viewcontent/sqj19.pdf
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