Classification and Persistence Analysis of Tie Strength on GitHub
Resumo
Relationships between users in social networks are evaluated in different ways. Here, our goal is to measure the strength of the ties between GitHub users by considering the temporal aspect of the network. Specifically, we analyze the evolution of these relationships by applying a classification algorithm over a GitHub network and calculate the persistence of them over different classes. The results bring new information about the collaborative software development process on the platform.
Referências
Gabriela B. Alves et al. 2016. The Strength of Social Coding Collaboration on GitHub. In SBBD. SBC, 247–252.
Carlos V. S. Araújo, Rayol M. Neto, Fabíola Guerra Nakamura, and Eduardo Freire Nakamura. 2017. Using Complex Networks to Assess Collaboration in Rap Music: A Study Case of DJ Khaled. In WebMedia. ACM, 425–428.
Natércia A. Batista et al. 2017. Collaboration strength metrics and analyses on GitHub. In WI. ACM, 170–178.
Natércia A. Batista, Guilherme A. de Sousa, Michele A. Brandão, Ana Paula Couto da Silva, and Mirella Moura Moro. 2018. Tie Strength Metrics to Rank Pairs of Developers from GitHub. J. Inf. Data Manag. 9, 1 (2018), 69–83. https://periodicos.ufmg.br/index.php/jidm/article/view/417
Michele A Brandão, Pedro OS Vaz de Melo, and Mirella M Moro. 2018. STACY: strength of ties automatic-classifier over the years. Journal of Information and Data Management 9, 1 (2018), 52–52.
Michele A. Brandão and Mirella M. Moro. 2017. Social professional networks: A survey and taxonomy. Comput. Commun. 100 (2017), 20–31.
Casey Casalnuovo, Bogdan Vasilescu, Premkumar T. Devanbu, and Vladimir Filkov. 2015. Developer onboarding in GitHub: the role of prior social links and language experience. In ESEC/SIGSOFT FSE. ACM, 817–828.
Laura A. Dabbish, H. Colleen Stuart, Jason Tsay, and James D. Herbsleb. 2012. Social coding in GitHub: transparency and collaboration in an open software repository. In CSCW. ACM, 1277–1286.
Georgios Gousios. 2013. The GHTorent Dataset and Tool Suite (MSR ’13). IEEE Press, 233–236.
Libo Li, Frank Goethals, Bart Baesens, and Monique Snoeck. 2017. Predicting software revision outcomes on GitHub using structural holes theory. Comput. Networks 114 (2017), 114–124.
Libo Li, Frank Goethals, Bart Baesens, and Monique Snoeck. 2017. Predicting software revision outcomes on GitHub using structural holes theory. Comput. Networks 114 (2017), 114–124.
Qiu Liqing, Yu Jinfeng, Fan Xin, Jia Wei, and Wenwen Gao. 2019. Analysis of Influence Maximization in Temporal Social Networks. IEEE Access 7 (2019), 42052–42062.
Michael Boyer O’Leary et al. 2011. Multiple team membership: A theoretical model of its effects on productivity and learning for individuals and teams. Academy of Management Review 36, 3 (2011), 461–478.
Gabriel P. Oliveira et al. 2018. Tie Strength in GitHub Heterogeneous Networks. In WebMedia. ACM, 363–370.
Leiming Ren, Shimin Shan, Xiujuan Xu, and Yu Liu. 2020. StarIn: An Approach to Predict the Popularity of GitHub Repository. In ICPCSEE (2) (Communications in Computer and Information Science), Vol. 1258. Springer, 258–273.
Anwar Said, Rabeeh Ayaz Abbasi, Onaiza Maqbool, Ali Daud, and Naif Radi Aljohani. 2018. CC-GA: A clustering coefficient based genetic algorithm for detecting communities in social networks. Appl. Soft Comput. 63 (2018), 59–70.
Jonathan Sillito and Eleanor Wynn. 2007. The Social Context of Software Main- tenance. In ICSM. IEEE Computer Society, 325–334.
Arlei Silva et al. 2011. From Individual Behavior to Influence Networks: A Case Study on Twitter. In WebMedia. SBC, 135–142.
Ferdian Thung et al. 2013. Network Structure of Social Coding in GitHub. In CSMR. IEEE Computer Society, 323–326.
Bogdan Vasilescu, Alexander Serebrenik, and Vladimir Filkov. 2015. A Data Set for Social Diversity Studies of GitHub Teams. In MSR. IEEE Computer Society, 514–517.
Tongfeng Weng, Yi Zhao, Michael Small, and Defeng David Huang. 2014. Time- series analysis of networks: Exploring the structure with random walks. Physical Review E. 90, 2 (2014), 022804.
Carlos V. S. Araújo, Rayol M. Neto, Fabíola Guerra Nakamura, and Eduardo Freire Nakamura. 2017. Using Complex Networks to Assess Collaboration in Rap Music: A Study Case of DJ Khaled. In WebMedia. ACM, 425–428.
Natércia A. Batista et al. 2017. Collaboration strength metrics and analyses on GitHub. In WI. ACM, 170–178.
Natércia A. Batista, Guilherme A. de Sousa, Michele A. Brandão, Ana Paula Couto da Silva, and Mirella Moura Moro. 2018. Tie Strength Metrics to Rank Pairs of Developers from GitHub. J. Inf. Data Manag. 9, 1 (2018), 69–83. https://periodicos.ufmg.br/index.php/jidm/article/view/417
Michele A Brandão, Pedro OS Vaz de Melo, and Mirella M Moro. 2018. STACY: strength of ties automatic-classifier over the years. Journal of Information and Data Management 9, 1 (2018), 52–52.
Michele A. Brandão and Mirella M. Moro. 2017. Social professional networks: A survey and taxonomy. Comput. Commun. 100 (2017), 20–31.
Casey Casalnuovo, Bogdan Vasilescu, Premkumar T. Devanbu, and Vladimir Filkov. 2015. Developer onboarding in GitHub: the role of prior social links and language experience. In ESEC/SIGSOFT FSE. ACM, 817–828.
Laura A. Dabbish, H. Colleen Stuart, Jason Tsay, and James D. Herbsleb. 2012. Social coding in GitHub: transparency and collaboration in an open software repository. In CSCW. ACM, 1277–1286.
Georgios Gousios. 2013. The GHTorent Dataset and Tool Suite (MSR ’13). IEEE Press, 233–236.
Libo Li, Frank Goethals, Bart Baesens, and Monique Snoeck. 2017. Predicting software revision outcomes on GitHub using structural holes theory. Comput. Networks 114 (2017), 114–124.
Libo Li, Frank Goethals, Bart Baesens, and Monique Snoeck. 2017. Predicting software revision outcomes on GitHub using structural holes theory. Comput. Networks 114 (2017), 114–124.
Qiu Liqing, Yu Jinfeng, Fan Xin, Jia Wei, and Wenwen Gao. 2019. Analysis of Influence Maximization in Temporal Social Networks. IEEE Access 7 (2019), 42052–42062.
Michael Boyer O’Leary et al. 2011. Multiple team membership: A theoretical model of its effects on productivity and learning for individuals and teams. Academy of Management Review 36, 3 (2011), 461–478.
Gabriel P. Oliveira et al. 2018. Tie Strength in GitHub Heterogeneous Networks. In WebMedia. ACM, 363–370.
Leiming Ren, Shimin Shan, Xiujuan Xu, and Yu Liu. 2020. StarIn: An Approach to Predict the Popularity of GitHub Repository. In ICPCSEE (2) (Communications in Computer and Information Science), Vol. 1258. Springer, 258–273.
Anwar Said, Rabeeh Ayaz Abbasi, Onaiza Maqbool, Ali Daud, and Naif Radi Aljohani. 2018. CC-GA: A clustering coefficient based genetic algorithm for detecting communities in social networks. Appl. Soft Comput. 63 (2018), 59–70.
Jonathan Sillito and Eleanor Wynn. 2007. The Social Context of Software Main- tenance. In ICSM. IEEE Computer Society, 325–334.
Arlei Silva et al. 2011. From Individual Behavior to Influence Networks: A Case Study on Twitter. In WebMedia. SBC, 135–142.
Ferdian Thung et al. 2013. Network Structure of Social Coding in GitHub. In CSMR. IEEE Computer Society, 323–326.
Bogdan Vasilescu, Alexander Serebrenik, and Vladimir Filkov. 2015. A Data Set for Social Diversity Studies of GitHub Teams. In MSR. IEEE Computer Society, 514–517.
Tongfeng Weng, Yi Zhao, Michael Small, and Defeng David Huang. 2014. Time- series analysis of networks: Exploring the structure with random walks. Physical Review E. 90, 2 (2014), 022804.
Publicado
30/11/2020
Como Citar
MOURA, Ana Flávia C.; OLIVEIRA, Gabriel P.; BRANDÃO, Michele A.; MORO, Mirella M..
Classification and Persistence Analysis of Tie Strength on GitHub. In: WORKSHOP DE TRABALHOS DE INICIAÇÃO CIENTÍFICA - SIMPÓSIO BRASILEIRO DE SISTEMAS MULTIMÍDIA E WEB (WEBMEDIA), 26. , 2020, São Luís.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2020
.
p. 41-44.
ISSN 2596-1683.
DOI: https://doi.org/10.5753/webmedia_estendido.2020.13059.
