Promises and Perils of Inferring Personality on GitHub
This work addresses the reliability of personality inference tools for software engineers, highlighting incremental improvements in error reduction.
The paper compared three text-based personality tests on GitHub data, finding an average error rate of 41% in inferring developer personality, with recommendations reducing it to 36% in best cases.
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. 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. 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. 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 recommendations.