Como os mantenedores usam GitHub Reactions? Um estudo exploratório
Resumo
Plataformas de codificação social modernas têm fomentado a comunicação e colaboração no desenvolvimento de software por meio de funcionalidades inspiradas de redes sociais tradicionais. Estudos anteriores mostraram que reactions é uma funcionalidade cada vez mais utilizada, contudo pouco se sabe sobre seu impacto no desenvolvimento de software da perspectiva dos desenvolvedores. Neste trabalho, é apresentado um estudo com 17 mantenedores de projetos populares na plataforma GitHub com intuito de coletar suas percepções sobre tal funcionalidade. Os resultados mostram que a absoluta maioria dos participantes vêem benefícios nas reações (e.g., praticidade de feedaback e métrica de aceitação) e três em cada quatro consideraram as reações ao tomarem decisões de projeto.
Palavras-chave:
Codificação Social, GitHub, Reações
Referências
Sebastian Baltes and Stephan Diehl. 2016. Worse than spam: Issues in sampling software developers. In 10th International Symposium on Empirical Software Engineering and Measurement (ESEM). 1-6.
Hudson Borges, Rodrigo Brito, and Marco Tulio Valente. 2019. Beyond Textual Issues: Understanding the usage and impact of GitHub reactions. In 33rd Brazilian Symposium on Software Engineering (SBES). 397-406.
Hudson Borges and Marco Tulio Valente. 2018. What's in a GitHub star? understanding repository starring practices in a social coding platform. Journal of Systems and Software (JSS) 146 (2018), 112-129.
Scott Chacon and Ben Straub. 2014. Pro git (2nd ed.). Springer Nature.
Daniela S Cruzes and Tore Dyba. 2011. Recommended steps for thematic synthesis in software engineering. In 5th International Symposium on Empirical Software Engineering and Measurement (ESEM). 275-284.
Laura Dabbish, Colleen Stuart, Jason Tsay, and Jim Herbsleb. 2012. Social coding in GitHub: transparency and collaboration in an open software repository. In 24th Conference on Computer Supported Cooperative Work (CSCW). 1277-1286.
Thomas Dimson. 2015. Emojineering: Machine learning for emoji trends by Instagram.
Chris Pool and Malvina Nissim. 2016. Distant supervision for emotion detection using Facebook reactions. In 1st Workshop on Computational Modeling of People's Opinions, Personality, and Emotions in Social Media (PEOPLES). 30-39.
Ferdian Thung, Tegawende F Bissyande, David Lo, and Lingxiao Jiang. 2013. Network structure of social coding in GitHub. In 17th European Conference on Software Maintenance and Reengineering (CSMR). 323-326.
Ye Tian, Thiago Galery, Giulio Dulcinati, Emilia Molimpakis, and Chao Sun. 2017. Facebook sentiment: Reactions and emojis. In 5th International Workshop on Natural Language Processing for Social Media (SocialNLP). 11-16.
Sarah Turnbull and Simon Jenkins. 2016. Why Facebook Reactions are good news for evaluating social media campaigns. Journal of Direct, Data and Digital Marketing Practice 17 (2016), 156-158.
Yang Zhang, HuaiminWang, Gang Yin, TaoWang, and Yue Yu. 2017. Social media in GitHub: the role of@-mention in assisting software development. Science China Information Sciences 60 (2017), 1-18.
Hudson Borges, Rodrigo Brito, and Marco Tulio Valente. 2019. Beyond Textual Issues: Understanding the usage and impact of GitHub reactions. In 33rd Brazilian Symposium on Software Engineering (SBES). 397-406.
Hudson Borges and Marco Tulio Valente. 2018. What's in a GitHub star? understanding repository starring practices in a social coding platform. Journal of Systems and Software (JSS) 146 (2018), 112-129.
Scott Chacon and Ben Straub. 2014. Pro git (2nd ed.). Springer Nature.
Daniela S Cruzes and Tore Dyba. 2011. Recommended steps for thematic synthesis in software engineering. In 5th International Symposium on Empirical Software Engineering and Measurement (ESEM). 275-284.
Laura Dabbish, Colleen Stuart, Jason Tsay, and Jim Herbsleb. 2012. Social coding in GitHub: transparency and collaboration in an open software repository. In 24th Conference on Computer Supported Cooperative Work (CSCW). 1277-1286.
Thomas Dimson. 2015. Emojineering: Machine learning for emoji trends by Instagram.
Chris Pool and Malvina Nissim. 2016. Distant supervision for emotion detection using Facebook reactions. In 1st Workshop on Computational Modeling of People's Opinions, Personality, and Emotions in Social Media (PEOPLES). 30-39.
Ferdian Thung, Tegawende F Bissyande, David Lo, and Lingxiao Jiang. 2013. Network structure of social coding in GitHub. In 17th European Conference on Software Maintenance and Reengineering (CSMR). 323-326.
Ye Tian, Thiago Galery, Giulio Dulcinati, Emilia Molimpakis, and Chao Sun. 2017. Facebook sentiment: Reactions and emojis. In 5th International Workshop on Natural Language Processing for Social Media (SocialNLP). 11-16.
Sarah Turnbull and Simon Jenkins. 2016. Why Facebook Reactions are good news for evaluating social media campaigns. Journal of Direct, Data and Digital Marketing Practice 17 (2016), 156-158.
Yang Zhang, HuaiminWang, Gang Yin, TaoWang, and Yue Yu. 2017. Social media in GitHub: the role of@-mention in assisting software development. Science China Information Sciences 60 (2017), 1-18.
Publicado
03/10/2022
Como Citar
NOVAIS, Pedro Lopez; FONTÃO, Awdren de Lima; BORGES, Hudson Silva; VALENTE, Marco Tulio.
Como os mantenedores usam GitHub Reactions? Um estudo exploratório. In: WORKSHOP DE VISUALIZAÇÃO, EVOLUÇÃO E MANUTENÇÃO DE SOFTWARE (VEM), 10. , 2022, Online.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2022
.
p. 21-25.
DOI: https://doi.org/10.5753/vem.2022.226591.
