A Dataset for Analysis of Quality Code and Toxic Comments

Jaime Sayago-Heredia; Gustavo Chango Sailema; Ricardo Pérez-Castillo; Mario Piattini

doi:10.1007/978-3-031-24985-3_41

A Dataset for Analysis of Quality Code and Toxic Comments

Jaime Sayago-Heredia, Gustavo Chango Sailema, Ricardo Pérez-Castillo, Mario Piattini

PUCE Esmeraldas

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Software development has an important human aspect, so it is known that the feelings of developers have a significant impact on software development and could affect the quality, productivity and performance of developers. In this study, we have begun the process of finding, understanding and relating these affects to software quality. We propose a quality code and sentiments dataset, a clean set of commits, code quality and toxic sentiments of 19 projects obtained from GitHub. The dataset extracts messages from the commits present in GitHub along with quality metrics from SonarQube. Using this information, we run machine learning techniques with the ML.Net tool to identify toxic developer sentiments in commits that could affect code quality. We analyzed 218K commits from the 19 selected projects. The analysis of the projects took 120 days. We also describe the process of building the tool and retrieving the data. The dataset will be used to further investigate in depth the factors that affect developers’ emotions and whether these factors are related to code quality in the life cycle of a software project. In addition, code quality will be estimated as a function of developer sentiments.

Original language	English
Title of host publication	Applied Technologies - 4th International Conference, ICAT 2022, Revised Selected Papers
Editors	Miguel Botto-Tobar, Marcelo Zambrano Vizuete, Sergio Montes León, Pablo Torres-Carrión, Benjamin Durakovic
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	559-574
Number of pages	16
ISBN (Print)	9783031249846
DOIs	https://doi.org/10.1007/978-3-031-24985-3_41
State	Published - 2023
Event	4th International Conference on Applied Technologies, ICAT 2022 - Quito, Ecuador Duration: 23 Nov 2022 → 25 Nov 2022

Publication series

Name	Communications in Computer and Information Science
Volume	1755 CCIS
ISSN (Print)	1865-0929
ISSN (Electronic)	1865-0937

Conference

Conference	4th International Conference on Applied Technologies, ICAT 2022
Country/Territory	Ecuador
City	Quito
Period	23/11/22 → 25/11/22

Bibliographical note

Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Keywords

Commits
GitHub
Sentiments analysis
Software Engineering
Software quality
SonarQube
Toxic comment classification

Access to Document

10.1007/978-3-031-24985-3_41

Cite this

Sayago-Heredia, J., Chango Sailema, G., Pérez-Castillo, R., & Piattini, M. (2023). A Dataset for Analysis of Quality Code and Toxic Comments. In M. Botto-Tobar, M. Zambrano Vizuete, S. Montes León, P. Torres-Carrión, & B. Durakovic (Eds.), Applied Technologies - 4th International Conference, ICAT 2022, Revised Selected Papers (pp. 559-574). (Communications in Computer and Information Science; Vol. 1755 CCIS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-24985-3_41

Sayago-Heredia, Jaime ; Chango Sailema, Gustavo ; Pérez-Castillo, Ricardo et al. / A Dataset for Analysis of Quality Code and Toxic Comments. Applied Technologies - 4th International Conference, ICAT 2022, Revised Selected Papers. editor / Miguel Botto-Tobar ; Marcelo Zambrano Vizuete ; Sergio Montes León ; Pablo Torres-Carrión ; Benjamin Durakovic. Springer Science and Business Media Deutschland GmbH, 2023. pp. 559-574 (Communications in Computer and Information Science).

@inproceedings{9225f73f093443978ff40e6e4daf5bd6,

title = "A Dataset for Analysis of Quality Code and Toxic Comments",

abstract = "Software development has an important human aspect, so it is known that the feelings of developers have a significant impact on software development and could affect the quality, productivity and performance of developers. In this study, we have begun the process of finding, understanding and relating these affects to software quality. We propose a quality code and sentiments dataset, a clean set of commits, code quality and toxic sentiments of 19 projects obtained from GitHub. The dataset extracts messages from the commits present in GitHub along with quality metrics from SonarQube. Using this information, we run machine learning techniques with the ML.Net tool to identify toxic developer sentiments in commits that could affect code quality. We analyzed 218K commits from the 19 selected projects. The analysis of the projects took 120 days. We also describe the process of building the tool and retrieving the data. The dataset will be used to further investigate in depth the factors that affect developers{\textquoteright} emotions and whether these factors are related to code quality in the life cycle of a software project. In addition, code quality will be estimated as a function of developer sentiments.",

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Sayago-Heredia, J, Chango Sailema, G, Pérez-Castillo, R & Piattini, M 2023, A Dataset for Analysis of Quality Code and Toxic Comments. in M Botto-Tobar, M Zambrano Vizuete, S Montes León, P Torres-Carrión & B Durakovic (eds), Applied Technologies - 4th International Conference, ICAT 2022, Revised Selected Papers. Communications in Computer and Information Science, vol. 1755 CCIS, Springer Science and Business Media Deutschland GmbH, pp. 559-574, 4th International Conference on Applied Technologies, ICAT 2022, Quito, Ecuador, 23/11/22. https://doi.org/10.1007/978-3-031-24985-3_41

A Dataset for Analysis of Quality Code and Toxic Comments. / Sayago-Heredia, Jaime; Chango Sailema, Gustavo; Pérez-Castillo, Ricardo et al.
Applied Technologies - 4th International Conference, ICAT 2022, Revised Selected Papers. ed. / Miguel Botto-Tobar; Marcelo Zambrano Vizuete; Sergio Montes León; Pablo Torres-Carrión; Benjamin Durakovic. Springer Science and Business Media Deutschland GmbH, 2023. p. 559-574 (Communications in Computer and Information Science; Vol. 1755 CCIS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Chango Sailema, Gustavo

AU - Pérez-Castillo, Ricardo

AU - Piattini, Mario

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N2 - Software development has an important human aspect, so it is known that the feelings of developers have a significant impact on software development and could affect the quality, productivity and performance of developers. In this study, we have begun the process of finding, understanding and relating these affects to software quality. We propose a quality code and sentiments dataset, a clean set of commits, code quality and toxic sentiments of 19 projects obtained from GitHub. The dataset extracts messages from the commits present in GitHub along with quality metrics from SonarQube. Using this information, we run machine learning techniques with the ML.Net tool to identify toxic developer sentiments in commits that could affect code quality. We analyzed 218K commits from the 19 selected projects. The analysis of the projects took 120 days. We also describe the process of building the tool and retrieving the data. The dataset will be used to further investigate in depth the factors that affect developers’ emotions and whether these factors are related to code quality in the life cycle of a software project. In addition, code quality will be estimated as a function of developer sentiments.

AB - Software development has an important human aspect, so it is known that the feelings of developers have a significant impact on software development and could affect the quality, productivity and performance of developers. In this study, we have begun the process of finding, understanding and relating these affects to software quality. We propose a quality code and sentiments dataset, a clean set of commits, code quality and toxic sentiments of 19 projects obtained from GitHub. The dataset extracts messages from the commits present in GitHub along with quality metrics from SonarQube. Using this information, we run machine learning techniques with the ML.Net tool to identify toxic developer sentiments in commits that could affect code quality. We analyzed 218K commits from the 19 selected projects. The analysis of the projects took 120 days. We also describe the process of building the tool and retrieving the data. The dataset will be used to further investigate in depth the factors that affect developers’ emotions and whether these factors are related to code quality in the life cycle of a software project. In addition, code quality will be estimated as a function of developer sentiments.

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T3 - Communications in Computer and Information Science

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A2 - Zambrano Vizuete, Marcelo

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Sayago-Heredia J, Chango Sailema G, Pérez-Castillo R, Piattini M. A Dataset for Analysis of Quality Code and Toxic Comments. In Botto-Tobar M, Zambrano Vizuete M, Montes León S, Torres-Carrión P, Durakovic B, editors, Applied Technologies - 4th International Conference, ICAT 2022, Revised Selected Papers. Springer Science and Business Media Deutschland GmbH. 2023. p. 559-574. (Communications in Computer and Information Science). doi: 10.1007/978-3-031-24985-3_41

A Dataset for Analysis of Quality Code and Toxic Comments

Abstract

Publication series

Conference

Bibliographical note

Keywords

Access to Document

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