A dataset for identifying actionable feedback in collaborative software development

Benjamin S. Meyers, Nuthan Munaiah, Emily Prud’hommeaux, Andrew Meneely, Josephine Wolff, Cecilia Ovesdotter Alm, Pradeep Murukannaiah


Abstract
Software developers and testers have long struggled with how to elicit proactive responses from their coworkers when reviewing code for security vulnerabilities and errors. For a code review to be successful, it must not only identify potential problems but also elicit an active response from the colleague responsible for modifying the code. To understand the factors that contribute to this outcome, we analyze a novel dataset of more than one million code reviews for the Google Chromium project, from which we extract linguistic features of feedback that elicited responsive actions from coworkers. Using a manually-labeled subset of reviewer comments, we trained a highly accurate classifier to identify acted-upon comments (AUC = 0.85). Our results demonstrate the utility of our dataset, the feasibility of using NLP for this new task, and the potential of NLP to improve our understanding of how communications between colleagues can be authored to elicit positive, proactive responses.
Anthology ID:
P18-2021
Volume:
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
Month:
July
Year:
2018
Address:
Melbourne, Australia
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
126–131
Language:
URL:
https://aclanthology.org/P18-2021
DOI:
10.18653/v1/P18-2021
Bibkey:
Cite (ACL):
Benjamin S. Meyers, Nuthan Munaiah, Emily Prud’hommeaux, Andrew Meneely, Josephine Wolff, Cecilia Ovesdotter Alm, and Pradeep Murukannaiah. 2018. A dataset for identifying actionable feedback in collaborative software development. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 126–131, Melbourne, Australia. Association for Computational Linguistics.
Cite (Informal):
A dataset for identifying actionable feedback in collaborative software development (Meyers et al., ACL 2018)
Copy Citation:
PDF:
https://aclanthology.org/P18-2021.pdf