Using Personality Detection Tools for Software Engineering Research: How Far Can We Go?

Calefato, Fabio; Lanubile, Filippo

doi:10.1145/3491039

Cited by 7 publications

(1 citation statement)

References 112 publications

(102 reference statements)

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“…However, inferring the dimensions of Openness and Neuroticism through automated methods presents challenges, and existing studies have reported low accuracy in predicting personality traits ( Bai et al, 2013 ; Adalı & Golbeck, 2014 ). This challenging prediction holds in software engineering context ( Calefato et al, 2018a ; Calefato & Lanubile, 2022 ). The estimated correlation between self-reported and predicted personality traits is generally below 0.5 ( Novikov, Mararitsa & Nozdrachev, 2021 ).…”

Section: Background and State Of The Artmentioning

confidence: 99%

Using social media and personality traits to assess software developers’ emotional polarity

Silva,

Gurgel de Castro,

Bernardino Silva

et al. 2023

PeerJ Computer Science

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Although human factors (e.g., cognitive functions, behaviors and skills, human error models, etc.) are key elements to improve software development productivity and quality, the role of software developers’ emotions and their personality traits in software engineering still needs to be studied. A major difficulty is in assessing developers’ emotions, leading to the classic problem of having difficulties understanding what cannot be easily measured. Existing approaches to infer emotions, such as facial expressions, self-assessed surveys, and biometric sensors, imply considerable intrusiveness on developers and tend to be used only during normal working periods. This article proposes to assess the feasibility of using social media posts (e.g., developers’ posts on Twitter) to accurately determine the polarity of emotions of software developers over extended periods in a non-intrusive manner, allowing the identification of potentially abnormal periods of negative or positive sentiments of developers that may affect software development productivity or software quality. Our results suggested that Twitter data can serve as a valid source for accurately inferring the polarity of emotions. We evaluated 31 combinations of unsupervised lexicon-based techniques using a dataset with 79,029 public posts from Twitter from sixteen software developers, achieving a macro F1-Score of 0.745 and 76.8% of accuracy with the ensemble comprised of SentiStrength, Sentilex-PT, and LIWC2015_PT lexicons. Among other results, we found a statistically significant difference in tweets’ polarities posted during working and non-working periods for 31.25% of the participants, suggesting that emotional polarity monitoring outside working hours could also be relevant. We also assessed the Big Five personality traits of the developers and preliminarily used them to ponder the polarities inferences. In this context, Openness, Conscientiousness, and Extraversion were frequently related to neutral and positive posts, while Neuroticism is associated with negative posts. Our results show that the proposed approach is accurate enough to constitute a simple and non-intrusive alternative to existing methods. Tools using this approach can be applied in real software development environments to support software team workers in making decisions to improve the software development process.

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