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    <titleInfo>
        <title>FLeCCS: A Technique for Suggesting Fragment-Level Similar Co-change Candidates</title>
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    <name type="personal">
        <namePart type="given">Manishankar</namePart>
        <namePart type="family">Mondal</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
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    <name type="personal">
        <namePart type="given">Chanchal</namePart>
        <namePart type="given">K</namePart>
        <namePart type="family">Roy</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
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    <name type="personal">
        <namePart type="given">Banani</namePart>
        <namePart type="family">Roy</namePart>
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            <roleTerm authority="marcrelator" type="text">author</roleTerm>
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    <name type="personal">
        <namePart type="given">Kevin</namePart>
        <namePart type="given">A</namePart>
        <namePart type="family">Schneider</namePart>
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            <roleTerm authority="marcrelator" type="text">author</roleTerm>
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        <dateIssued>2021</dateIssued>
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            <title>2021 IEEE/ACM 29th International Conference on Program Comprehension (ICPC)</title>
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            <issuance>continuing</issuance>
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    <abstract>When a programmer changes a particular code fragment, the other similar code fragments in the code-base may also need to be changed together (i.e., co-changed) consistently to ensure that the software system remains consistent. Existing studies and tools apply clone detectors to identify these similar co-change candidates for a target code fragment. However, clone detectors suffer from a confounding configuration choice problem and it affects their accuracy in retrieving co-change candidates.In our research, we propose and empirically evaluate a lightweight co-change suggestion technique that can automatically suggest fragment level similar co-change candidates for a target code fragment using WA-DiSC (Weighted Average Dice-Sørensen Co-efficient) through a context-sensitive mining of the entire code-base. We apply our technique, FLeCCS (Fragment Level Co-change Candidate Suggester), on six subject systems written in three different programming languages (Java, C, and C#) and compare its performance with the existing state-of-the-art techniques. According to our experiment, our technique outperforms not only the existing code clone based techniques but also the association rule mining based techniques in detecting co-change candidates with a significantly higher accuracy (precision and recall). We also find that File Proximity Ranking performs significantly better than Similarity Extent Ranking when ranking the co-change candidates suggested by our proposed technique.</abstract>
    <identifier type="citekey">Mondal-2021-FLeCCS:</identifier>
    <identifier type="doi">10.1109/icpc52881.2021.00024</identifier>
    <location>
        <url>https://gwf-uwaterloo.github.io/gwf-publications/G21-24001</url>
    </location>
    <part>
        <date>2021</date>
    </part>
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%0 Journal Article
%T FLeCCS: A Technique for Suggesting Fragment-Level Similar Co-change Candidates
%A Mondal, Manishankar
%A Roy, Chanchal K.
%A Roy, Banani
%A Schneider, Kevin A.
%J 2021 IEEE/ACM 29th International Conference on Program Comprehension (ICPC)
%D 2021
%I IEEE
%F Mondal-2021-FLeCCS:
%X When a programmer changes a particular code fragment, the other similar code fragments in the code-base may also need to be changed together (i.e., co-changed) consistently to ensure that the software system remains consistent. Existing studies and tools apply clone detectors to identify these similar co-change candidates for a target code fragment. However, clone detectors suffer from a confounding configuration choice problem and it affects their accuracy in retrieving co-change candidates.In our research, we propose and empirically evaluate a lightweight co-change suggestion technique that can automatically suggest fragment level similar co-change candidates for a target code fragment using WA-DiSC (Weighted Average Dice-Sørensen Co-efficient) through a context-sensitive mining of the entire code-base. We apply our technique, FLeCCS (Fragment Level Co-change Candidate Suggester), on six subject systems written in three different programming languages (Java, C, and C#) and compare its performance with the existing state-of-the-art techniques. According to our experiment, our technique outperforms not only the existing code clone based techniques but also the association rule mining based techniques in detecting co-change candidates with a significantly higher accuracy (precision and recall). We also find that File Proximity Ranking performs significantly better than Similarity Extent Ranking when ranking the co-change candidates suggested by our proposed technique.
%R 10.1109/icpc52881.2021.00024
%U https://gwf-uwaterloo.github.io/gwf-publications/G21-24001
%U https://doi.org/10.1109/icpc52881.2021.00024

Markdown (Informal)

[FLeCCS: A Technique for Suggesting Fragment-Level Similar Co-change Candidates](https://gwf-uwaterloo.github.io/gwf-publications/G21-24001) (Mondal et al., GWF 2021)

• FLeCCS: A Technique for Suggesting Fragment-Level Similar Co-change Candidates (Mondal et al., GWF 2021)

ACL

• Manishankar Mondal, Chanchal K. Roy, Banani Roy, and Kevin A. Schneider. 2021. FLeCCS: A Technique for Suggesting Fragment-Level Similar Co-change Candidates. 2021 IEEE/ACM 29th International Conference on Program Comprehension (ICPC).