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    <titleInfo>
        <title>Supporting program comprehension by generating abstract code summary tree</title>
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    <name type="personal">
        <namePart type="given">Avijit</namePart>
        <namePart type="family">Bhattacharjee</namePart>
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            <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>
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        <namePart type="family">Schneider</namePart>
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            <title>Proceedings of the ACM/IEEE 44th International Conference on Software Engineering: New Ideas and Emerging Results</title>
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    <abstract>Reading through code, finding relevant methods, classes and files takes a significant portion of software development time. Having good tool support for this code browsing activity can reduce human effort and increase overall developer productivity. To help with program comprehension activities, building an abstract code summary of a software system from its call graph is an active research area. A call graph is a visual representation of the caller-callee relationships between different methods of a software system. Call graphs can be difficult to comprehend for a large code-base. Previous work by Gharibi et al. on abstract code summarizing suggested using the Agglomerative Hierarchical Clustering (AHC) tree for understanding the codebase. Each node in the tree is associated with the top five method names. When we replicated the previous approach, we observed that the number of nodes in the AHC tree is burdensome for developers to explore. We also noticed only five method names for each node is not sufficient to comprehend an abstract node. We propose a technique to transform the AHC tree using cluster flattening for natural grouping and reduced nodes. We also generate a natural text summary for each abstract node derived from method comments. In order to evaluate our proposed approach, we collected developers’ opinions about the abstract code summary tree based on their codebase. The evaluation results confirm that our approach can not only help developers get an overview of their codebases but also could assist them in doing specific software maintenance tasks.</abstract>
    <identifier type="citekey">Bhattacharjee-2022-Supporting</identifier>
    <identifier type="doi">10.1145/3510455.3512793</identifier>
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        <url>https://gwf-uwaterloo.github.io/gwf-publications/G22-8001</url>
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        <date>2022</date>
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%0 Journal Article
%T Supporting program comprehension by generating abstract code summary tree
%A Bhattacharjee, Avijit
%A Roy, Banani
%A Schneider, Kevin A.
%J Proceedings of the ACM/IEEE 44th International Conference on Software Engineering: New Ideas and Emerging Results
%D 2022
%I ACM
%F Bhattacharjee-2022-Supporting
%X Reading through code, finding relevant methods, classes and files takes a significant portion of software development time. Having good tool support for this code browsing activity can reduce human effort and increase overall developer productivity. To help with program comprehension activities, building an abstract code summary of a software system from its call graph is an active research area. A call graph is a visual representation of the caller-callee relationships between different methods of a software system. Call graphs can be difficult to comprehend for a large code-base. Previous work by Gharibi et al. on abstract code summarizing suggested using the Agglomerative Hierarchical Clustering (AHC) tree for understanding the codebase. Each node in the tree is associated with the top five method names. When we replicated the previous approach, we observed that the number of nodes in the AHC tree is burdensome for developers to explore. We also noticed only five method names for each node is not sufficient to comprehend an abstract node. We propose a technique to transform the AHC tree using cluster flattening for natural grouping and reduced nodes. We also generate a natural text summary for each abstract node derived from method comments. In order to evaluate our proposed approach, we collected developers’ opinions about the abstract code summary tree based on their codebase. The evaluation results confirm that our approach can not only help developers get an overview of their codebases but also could assist them in doing specific software maintenance tasks.
%R 10.1145/3510455.3512793
%U https://gwf-uwaterloo.github.io/gwf-publications/G22-8001
%U https://doi.org/10.1145/3510455.3512793

Markdown (Informal)

[Supporting program comprehension by generating abstract code summary tree](https://gwf-uwaterloo.github.io/gwf-publications/G22-8001) (Bhattacharjee et al., GWF 2022)

• Supporting program comprehension by generating abstract code summary tree (Bhattacharjee et al., GWF 2022)

ACL

• Avijit Bhattacharjee, Banani Roy, and Kevin A. Schneider. 2022. Supporting program comprehension by generating abstract code summary tree. Proceedings of the ACM/IEEE 44th International Conference on Software Engineering: New Ideas and Emerging Results.