A large-scale, in-depth analysis of developers’ personalities in the Apache ecosystem

Abstract

Context: Large-scale distributed projects are typically the results of collective efforts performed by multiple developers with heterogeneous personalities.Objective: We aim to find evidence that personalities can explain developers' behavior in large scale-distributed projects. For example, the propensity to trust others -a critical factor for the success of global software engineering -has been found to influence positively the result of code reviews in distributed projects.Method: In this paper, we perform a quantitative analysis of ecosystem-level data from the code commits and email messages contributed by the developers working on the Apache Software Foundation (ASF) projects, as representative of large scale-distributed projects.Results: We find that there are three common types of personality profiles among Apache developers, characterized in particular by their level of Agreeableness and Neuroticism. We also confirm that developers' personality is stable over time. Moreover, personality traits do not vary with their role, membership, and extent of contribution to the projects. We also find evidence that more open developers are more likely to make contributors to Apache projects.Conclusion: Overall, our findings reinforce the need for future studies on human factors in software engineering to use psychometric tools to control for differences in developers' personalities.

Promises and Perils of Inferring Personality on GitHub

“…In terms of the accuracy of automatically extracting personality traits from text, we compare our work against the large-scale analysis of personality in software engineering by Calefato et al [10]. In their comparison of personality models, they observe an accuracy ranging from 40-70%, which is globally in line with our own results.…”

“…Many studies in software engineering (e.g., [10,42,44]) and beyond (e.g., [23]) have used psycholinguistic tests for inferring personality. In software engineering, one of the first studies showed that communications data can be used to infer personality [46].…”

“…In software engineering, one of the first studies showed that communications data can be used to infer personality [46]. Today, software communications data are used to infer developer personality (individually and as a group) to characterize developers and their participation patterns [10,44], and its effect on project success [63], including intermediate steps such as pull request acceptance [30].…”

“…Our survey comprises 44 questions indicating personality (Openness (10), Extraversion (8), Agreeableness (9), Conscientiousness (9), and Neuroticism (8)). An example question in our survey is: 'I am someone who is talkative.'…”

“…We recommend that with an appropriate amount of text size, all the models perform similarly. This is particularly helpful now that PI is deprecated 10 a widely used option recently.…”