Majumdar, S., Bansal, A., Das, P.P. et al. (3 more authors) (2022) Automated evaluation of comments to aid software maintenance. Journal of Software: Evolution and Process, 34 (7). e2463. ISSN 2047-7473
Abstract
Approaches to evaluate comments based on whether they increase code comprehensibility for software maintenance tasks are important, but largely missing. We propose Comment Probe for automated classification and quality evaluation of code comments of C codebases based on how they can help to understand existing code. We conduct surveys and document developers' perceptions on the type of comments that prove useful to maintaining software in the form of comment categories. A total of 20,206 comments have been collected from open-source Github projects and annotated with assistance from industry experts. We develop features to semantically analyze comments to locate concepts related to categories of usefulness. Additionally, features based on code and comment correlation are designed to infer whether the comment is also consistent and not superfluous. Using neural networks, comments are classified as useful, partially useful, and not useful with precision and recall scores of 86.27% and 86.42%, respectively. The proposed framework for comment quality evaluation incorporates industry practices and adds significant value to companies wanting to formulate better code commenting strategies. Furthermore, large codebases can be de-cluttered by removing comments not helpful in maintaining code.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2022 John Wiley & Sons Ltd. This is an author-produced version of a paper subsequently published in Journal of Software: Evolution and Process. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | code comprehension; comment quality; knowledge graph; machine learning; ontology |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Social Sciences (Sheffield) > Information School (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 30 Sep 2022 11:02 |
Last Modified: | 27 May 2023 00:13 |
Status: | Published |
Publisher: | Wiley |
Refereed: | Yes |
Identification Number: | 10.1002/smr.2463 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:191386 |