Promises and Perils of Inferring Personality on GitHub

Promises and Perils of Inferring Personality on GitHub

van Mil, Frenk (Student TU Delft)
Rastogi, A. (Rijksuniversiteit Groningen; Student TU Delft)
Zaidman, A.E. (TU Delft Software Engineering)

2021

Background: Personality plays a pivotal role in our understanding of human actions and behavior. Today, the applications of personality are widespread, built on the solutions from psychology to infer personality. Aim: In software engineering, for instance, one widely used solution to infer personality uses textual communication data. As studies on personality in software engineering continue to grow, it is imperative to understand the performance of these solutions. Method: This paper compares the inferential ability of three widely studied text-based personality tests against each other and the ground truth on GitHub. We explore the challenges and potential solutions to improve the inferential ability of personality tests. Results: Our study shows that solutions for inferring personality are far from being perfect. Software engineering communications data can infer individual developer personality with an average error rate of 41%. In the best case, the error rate can be reduced up to 36% by following our recommendations1.

LIWC
Mining Software Repositories
Personality
Personality Insights
Software Developer

http://resolver.tudelft.nl/uuid:328b1db7-aa63-4bb4-8d97-695652fd1ad7

https://doi.org/10.1145/3475716.3475775

Association for Computing Machinery (ACM)

2022-07-01

9781450386654

ESEM '21: Proceedings of the 15th ACM / IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM)

Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Institutional Repository

conference paper

© 2021 Frenk van Mil, A. Rastogi, A.E. Zaidman