Detecting Interpersonal Conflict in Issues and Code Review: Cross Pollinating Open- and Closed-Source Approaches

kaestner@cs.cmu.edu (H. Qiu, B. Vasilescu, C. Kästner, C. Egelman, C. Jaspan, E. Murphy-Hill)
Tue, 31 May 2022 12:28:28 GMT

Huilian Sophie Qiu, Bogdan Vasilescu, Christian Kästner, Carolyn Egelman, Ciera Jaspan, and Emerson Murphy-Hill. Detecting Interpersonal Conflict in Issues and Code Review: Cross Pollinating Open- and Closed-Source Approaches. In Proceedings of the Proc. International Conference on Software Engineering -- Software Engineering in Society Track (ICSE-SEIS), pages 41--55, New York, NY: ACM Press, May 2022.

Interpersonal conflict in code review, such as toxic language or an unnecessary pushback, is associated with negative outcomes such as stress and turnover. Automatic detection is one approach to prevent and mitigate interpersonal conflict. Two recent automatic detection approaches were developed in different settings: a toxicity detector using text analytics for open source issue discussions and a pushback detector using logs-based metrics for corporate code reviews. This paper tests how the toxicity detector and the pushback detector can be generalized beyond their respective contexts and discussion types, and how the combination of the two can help improve interpersonal conflict detection. The results reveal connections between the two concepts.