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Escuela de Sistemas y Computación, Pontificia Universidad Católica del Ecuador, Sede Esmeraldas, Espejo y subida a Santa Cruz Casilla 08-01-0065, Ecuador

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1 Papers

Exploring the Impact of Toxic Comments in Code Quality

Topics: Empirical Software Engineering; Quality Management; Maintainability

Authors: Jaime Sayago-Heredia ¹ , Gustavo Chango ¹ , Ricardo Pérez-Castillo ² and Mario Piattini ³

Affiliations: ¹ Escuela de Sistemas y Computación, Pontificia Universidad Católica del Ecuador, Sede Esmeraldas, Espejo y subida a Santa Cruz Casilla 08-01-0065, Ecuador , ² Facultad de Ciencias Sociales de Talavera de la Reina, University of Castilla-La Mancha, Avenida Real Fábrica de Seda s/n 45600, Talavera de la Reina, Spain , ³ Information Technology & Systems Institute (ITSI), University of Castilla-La Mancha, Paseo de la Universidad 4, 13071, Ciudad Real, Spain

Digital Object Identifier: 10.5220/0011039700003176

Proceedings of ENASE 2022, ISBN: 978-989-758-568-5

Quick Abstract

Software development has an important human-side, which implies that developers' feelings have a significant impact to software development and could affect developers' quality, productivity, and performance. In this paper, we explore the process to find, understand and relate the effects of toxic emotions on code quality. We propose a tool and sentiments dataset, a clean set of commit messages, extracted from SonarQube code quality metrics and toxic comments obtained from GitHub. Moreover, we perform a preliminary statistical analysis of the dataset. We apply natural language processing techniques to identify toxic developer sentiments on commits that could impact code quality. Our study describes data retrieval process along with tools used for performing a preliminary analysis. The preliminary dataset is available in CSV format to facilitate queries on the data and to investigate in depth factors that impact developer emotions. Preliminary results imply that there is a relationship between toxic comments and code quality that may affect the quality of the software project. Future research will be the development of a complete dataset and an in-depth analysis for efficiency validation experiments along with a linear regression. Finally, we will estimate the code quality as a function of developers' toxic comments.