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.
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|Characterizing and Predicting Good First Issues|
Yuekai Huang Institute of Software, Chinese Academy of Sciences, Junjie Wang Institute of Software at Chinese Academy of Sciences, Song Wang York University, Zhe Liu Institute of Software at Chinese Academy of Sciences, Dandan Wang Institute of Software, Chinese Academy of Sciences, Qing Wang Institute of Software at Chinese Academy of SciencesPre-print
|An Empirical Study on Refactoring-Inducing Pull Requests|
Flavia Coelho Federal University of Campina Grande, Nikolaos Tsantalis Concordia University, Tiago Massoni Federal University of Campina Grande, Everton L. G. Alves Federal University of Campina GrandePre-print Media Attached
|Promises and Perils of Inferring Personality on GitHub|
Frenk van Mil Delft University of Technology, Ayushi Rastogi University of Groningen, The Netherlands, Andy Zaidman Delft University of TechnologyPre-print Media Attached
|An Exploratory Study on Dead Methods in Open-source Java Desktop Applications|