| Title | Where should I comment my code? A dataset and model for predicting locations that need comments |
| Publication Type | Conference Paper |
| Year of Publication | 2020 |
| Authors | Louis A, Dash SKumar, Barr ET, Ernst MD, Sutton C |
| Conference Name | ICSE NIER, Proceedings of the 42nd International Conference on Software Engineering, New Ideas and Emerging Results Track |
| Pagination | 21-24 |
| Date or Month Published | May |
| Conference Location | Seoul, Korea |
| Abstract | Programmers should write code comments, but not on every line of code. 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. comment-worthy locations. This first success opens the door to future work, both in the new \emphwhere-to-comment problem and in guiding comment generation. Our code and data is available at \urlhttps://groups.inf.ed.ac.uk/cup/comment-locator/. |
| Downloads | https://groups.inf.ed.ac.uk/cup/comment-locator/ implementation and dataset
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| Citation Key | LouisDBES2020 |
