Fabiyi, S. orcid.org/0000-0001-9571-2964 and Ajibuwa, O. (2023) Automatic Code Commenting in Integrated Development Environments Based on Indirect Interaction with Chatbots. In: 2023 International Scientific Conference on Computer Science (COMSCI). 2023 11-th International Scientific Conference COMPUTER SCIENCE, 18-20 Sep 2023, Hotel Lazur, Sozopol, Bulgaria. IEEE ISBN 979-8-3503-2526-3
Abstract
Efficient code commenting is critical to improving code readability, maintainability, and collaboration. This paper introduces automated code commenting within an integrated development environment IDE using chatbots. The introduced system, implemented in Python with Selenium, automates the comments generation process, allowing coders to focus on code logic. Results obtained demonstrates successful interactions with chatbots, comment retrieval and handling delay. Challenges identified include instruction selection and extended conversations, offering opportunities for improvement. Future prospects include reusable libraries, user-friendly interfaces, and streamlined code-commenting.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | This is an author produced version of a conference paper accepted to the 2023 11-th International Scientific Conference COMPUTER SCIENCE, made available under the terms of the Creative Commons Attribution License (CC-BY), which permits unrestricted use, distribution and reproduction in any medium, provided the original work is properly cited. |
Keywords: | automation, chatbot, comment, code, selenium |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 05 Oct 2023 15:38 |
Last Modified: | 21 Nov 2023 13:58 |
Published Version: | https://ieeexplore.ieee.org/document/10315818 |
Status: | Published |
Publisher: | IEEE |
Identification Number: | 10.1109/COMSCI59259.2023.10315818 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:202981 |