Proceedings Article10.1109/ICSME46990.2020.00115
Learning based and Context Aware Non-Informative Comment Detection
Mingwei Liu,Yanjun Yang,Xin Peng,Chong Wang,Chengyuan Zhao,Xin Wang,Shuangshuang Xing +6 more
- 01 Sep 2020
- pp 866-867
4
TL;DR: The approach that is introduced is designed and implemented for the DeClutter challenge of Doc-Gen2, which detects non-informative code comments, and combines both comment based text classification and code context based prediction.
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Abstract: This report introduces the approach that we have designed and implemented for the DeClutter challenge of Doc-Gen2, which detects non-informative code comments. The approach combines both comment based text classification and code context based prediction. Based on the approach, our "fduse" team achieved the best F1 score (0.847) in the competition.
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Proceedings Article
Overview of the IRSE track at FIRE 2022: Information Retrieval in Software Engineering
Srijoni Majumdar,Ayan Bandyopadhyay,Samiran Chattopadhyay,Partha-Pratim Das,Paul Clough,Prasenjit Majumder +5 more
TL;DR: In this article , the Information Retrieval in Software Engineering (IRSE) track aims to develop solutions for automated evaluation of code comments and there is a binary classification task to classify comments as useful and not useful.
Leveraging Generative AI: Improving Software Metadata Classification with Generated Code-Comment Pairs
TL;DR: This study showcases the potential of generative AI for enhancing binary code comment quality classification models, providing valuable insights for software developers and researchers in the field of natural language processing and software engineering.
Can we predict useful comments in source codes? - Analysis of findings from Information Retrieval in Software Engineering Track @ FIRE 2022
Srijoni Majumdar,Ayan Bandyopadhyay,Partha Pratim Das,Paul Clough,Samiran Chattopadhyay,Prasenjit Majumder +5 more
- 09 Dec 2022
TL;DR: In this paper , the Information Retrieval in Software Engineering (IRSE) track aims to develop solutions for automated evaluation of code comments in a machine learning framework and there is a binary classification task to classify comments as useful and not useful.
Automated evaluation of comments to aid software maintenance
TL;DR: Comment Probe proposes Comment Probe for automated classification and quality evaluation of code comments of C codebases based on how they can help to understand existing code.
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