Investigando a Relação das Refatorações de Código com os Sentimentos de Mensagens de Commit
Resumo
Este trabalho apresenta um estudo a partir da análise de sentimentos em mensagens expressas pelos desenvolvedores por meio de commits em repositórios de código aberto. Foram analisados seis projetos de código aberto em um total de 12.113 mensagens de commit. Para o estudo foram classificadas as mensagens dos commits em duas categorias: positivo ou negativo. Em seguida, e realizada uma investigação se os sentimentos estão relacionados às atividades de refatoração de código. Como resultados deste estudo, tem-se indícios que quando se trabalha com refatorações a tendência é a expressar menos sentimentos negativos.
Palavras-chave:
Refatoração de código, commit, análise de sentimentos, classificação
Referências
De Choudhury, M. and Counts, S. (2013). Understanding affect in the workplace via social media. In Proceedings of the 2013 Conference on Computer Supported Cooperative Work, CSCW ’13, pages 303–316, New York, NY, USA. ACM.
De Winter, J. C. (2013). Using the student’s t-test with extremely small sample sizes. Practical Assessment, Research & Evaluation, 18(10).
Fowler, M. and Beck, K. (1999). Refactoring: improving the design of existing code. Addison-Wesley Professional.
Gehan, E. A. (1965). A generalized wilcoxon test for comparing arbitrarily singly-censored samples. Biometrika, 52(1-2):203–224.
Graziotin, D., Wang, X., and Abrahamsson, P. (2014). Happy software developers solve problems better: psychological measurements in empirical software engineering. PeerJ, 2:e289.
Guzman, E., Azocar, D., and Li, Y. (2014). Sentiment analysis of commit comments in github: An empirical study. In Proceedings of the 11th Working Conference on Mining Software Repositories, MSR 2014, pages 352–355, New York, NY, USA. ACM.
Singh, N. and Singh, P. (2017). How do code refactoring activities impact software developers sentiments? – an empirical investigation into github commits. In 2017 24th Asia-Pacific Software Engineering Conference (APSEC), pages 648–653. IEEE.
Sinha, V., Lazar, A., and Sharif, B. (2016). Analyzing developer sentiment in commit logs. In Proceedings of the 13th International Conference on Mining Software Repositories, pages 520–523. ACM.
Thelwall, M., Buckley, K., and Paltoglou, G. (2012). Sentiment strength detection for the social web. Journal of the Association for Information Science and Technology, 63(1):163–173.
Tsantalis, N., Guana, V., Stroulia, E., and Hindle, A. (2013). A multidimensional empirical study on refactoring activity. In Proceedings of the 2013 Conference of the Center for Advanced Studies on Collaborative Research, pages 132–146. IBM Corp.
Tsantalis, N., Mansouri, M., Eshkevari, L., Mazinanian, D., and Dig, D. Accurate and efficient refactoring detection in commit history. In Proceedings of the 40th International Conference on Software Engineering, ICSE 2018.
Xing, Z. and Stroulia, E. (2005). Umldiff: an algorithm for object-oriented design differencing. In Proceedings of the 20th IEEE/ACM international Conference on Automated software engineering, pages 54–65. ACM
De Winter, J. C. (2013). Using the student’s t-test with extremely small sample sizes. Practical Assessment, Research & Evaluation, 18(10).
Fowler, M. and Beck, K. (1999). Refactoring: improving the design of existing code. Addison-Wesley Professional.
Gehan, E. A. (1965). A generalized wilcoxon test for comparing arbitrarily singly-censored samples. Biometrika, 52(1-2):203–224.
Graziotin, D., Wang, X., and Abrahamsson, P. (2014). Happy software developers solve problems better: psychological measurements in empirical software engineering. PeerJ, 2:e289.
Guzman, E., Azocar, D., and Li, Y. (2014). Sentiment analysis of commit comments in github: An empirical study. In Proceedings of the 11th Working Conference on Mining Software Repositories, MSR 2014, pages 352–355, New York, NY, USA. ACM.
Singh, N. and Singh, P. (2017). How do code refactoring activities impact software developers sentiments? – an empirical investigation into github commits. In 2017 24th Asia-Pacific Software Engineering Conference (APSEC), pages 648–653. IEEE.
Sinha, V., Lazar, A., and Sharif, B. (2016). Analyzing developer sentiment in commit logs. In Proceedings of the 13th International Conference on Mining Software Repositories, pages 520–523. ACM.
Thelwall, M., Buckley, K., and Paltoglou, G. (2012). Sentiment strength detection for the social web. Journal of the Association for Information Science and Technology, 63(1):163–173.
Tsantalis, N., Guana, V., Stroulia, E., and Hindle, A. (2013). A multidimensional empirical study on refactoring activity. In Proceedings of the 2013 Conference of the Center for Advanced Studies on Collaborative Research, pages 132–146. IBM Corp.
Tsantalis, N., Mansouri, M., Eshkevari, L., Mazinanian, D., and Dig, D. Accurate and efficient refactoring detection in commit history. In Proceedings of the 40th International Conference on Software Engineering, ICSE 2018.
Xing, Z. and Stroulia, E. (2005). Umldiff: an algorithm for object-oriented design differencing. In Proceedings of the 20th IEEE/ACM international Conference on Automated software engineering, pages 54–65. ACM
Publicado
25/08/2018
Como Citar
DE SOUZA, Jordão M.; SILVA, Ticiana L. Coelho da; DE SOUZA, Criston P.; MOREIRA, Carla Ilane; ROCHA, Lincoln; MACÊDO, José Antônio F. de.
Investigando a Relação das Refatorações de Código com os Sentimentos de Mensagens de Commit. In: SIMPÓSIO BRASILEIRO DE BANCO DE DADOS (SBBD), 33. , 2018, Rio de Janeiro.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2018
.
p. 253-258.
ISSN 2763-8979.
DOI: https://doi.org/10.5753/sbbd.2018.22239.
