ICSE 2020 (series) / New Ideas and Emerging Results / Where should I comment my code? A dataset and model for predicting locations that need comments
Where should I comment my code? A dataset and model for predicting locations that need commentsNIER
Programmers should write code comments, but not on every line of code. Because both too few and too many comments are undesirable, programmers must judiciously decide where to write code comments. We have created a machine learning model that suggests locations where a programmer should write a code comment. We trained it on existing commented code to learn locations that are chosen by developers. Once trained, the model can predict locations in new code. Our models achieved precision of 74% and recall of 13% in identifying comment-worthy locations. This first success opens the door to future work, both in the new \emph{where-to-comment} problem and in generating the content of comments.
Tue 7 JulDisplayed time zone: (UTC) Coordinated Universal Time change
Tue 7 Jul
Displayed time zone: (UTC) Coordinated Universal Time change
15:00 - 16:00 | A3-Code SummarizationTechnical Papers / New Ideas and Emerging Results at Silla Chair(s): Shaohua Wang New Jersey Institute of Technology, USA | ||
15:00 12mTalk | Posit: Simultaneously Tagging Natural and Programming LanguagesTechnical Technical Papers Profir-Petru Pârțachi University College London, Santanu Dash University College London, UK, Christoph Treude The University of Adelaide, Earl T. Barr University College London, UK Pre-print Media Attached File Attached | ||
15:12 12mTalk | CPC: Automatically Classifying and Propagating Natural Language Comments via Program AnalysisTechnical Technical Papers Juan Zhai Rutgers University, Xiangzhe Xu Nanjing University, Yu Shi Purdue University, Guanhong Tao Purdue University, Minxue Pan Nanjing University, Shiqing Ma Rutgers University, Lei Xu National Key Laboratory for Novel Software Technology, Nanjing University, Weifeng Zhang Nanjing University of Posts and Telecommunications, Lin Tan Purdue University, Xiangyu Zhang Purdue University | ||
15:24 12mTalk | Suggesting Natural Method Names to Check Name ConsistenciesTechnical Technical Papers Son Nguyen The University of Texas at Dallas, Hung Phan , Trinh Le University of Engineering and Technology, Tien N. Nguyen University of Texas at Dallas Pre-print | ||
15:36 6mTalk | Where should I comment my code? A dataset and model for predicting locations that need commentsNIER New Ideas and Emerging Results Annie Louis University of Edinburgh, Santanu Dash University College London, UK, Earl T. Barr University College London, UK, Michael D. Ernst University of Washington, USA, Charles Sutton Google Research | ||
15:42 12mTalk | Retrieval-based Neural Source Code SummarizationTechnical Technical Papers Jian Zhang Beihang University, Xu Wang Beihang University, Hongyu Zhang University of Newcastle, Australia, Hailong Sun Beihang University, Xudong Liu Beihang University Pre-print | ||
15:54 6mTalk | The Dual Channel HypothesisNIER New Ideas and Emerging Results Casey Casalnuovo University of California at Davis, USA, Earl T. Barr University College London, UK, Santanu Dash University College London, UK, Prem Devanbu University of California, Emily Morgan University of California, Davis | ||