TY - GEN
T1 - Using structural holes metrics from communication networks to predict change dependencies
AU - Scaliante Wiese, Igor
AU - Takashi Kuroda, Rodrigo
AU - Nassif Roma, Douglas
AU - Ré, Reginaldo
AU - Ansaldi Oliva, Gustavo
AU - Aurelio Gerosa, Marco
PY - 2014
Y1 - 2014
N2 - Conway's Law describes that software systems are structured according to the communication structures of their developers. These developers when working on a feature or correcting a bug commit together a set of source code artifacts. The analysis of these co-changes makes it possible to identify change dependencies between artifacts. Influenced by Conway's Law, we hypothesize that Structural Hole Metrics (SHM) are able to identify strong and weak change coupling. We used SHM computed from communication networks to predict co-changes among files. Comparing SHM against process metrics using six well-known classification algorithms applied to Rails and Node.js projects, we achieved recall and precision values near 80% in the best cases. Mathews Correlation metric was used to verify if SHM was able to identify strong and weak co-changes. We also extracted rules to provide insights about the metrics using classification tree. To the best of our knowledge, this is the first study that investigated social aspects to predict change dependencies and the results obtained are very promising.
AB - Conway's Law describes that software systems are structured according to the communication structures of their developers. These developers when working on a feature or correcting a bug commit together a set of source code artifacts. The analysis of these co-changes makes it possible to identify change dependencies between artifacts. Influenced by Conway's Law, we hypothesize that Structural Hole Metrics (SHM) are able to identify strong and weak change coupling. We used SHM computed from communication networks to predict co-changes among files. Comparing SHM against process metrics using six well-known classification algorithms applied to Rails and Node.js projects, we achieved recall and precision values near 80% in the best cases. Mathews Correlation metric was used to verify if SHM was able to identify strong and weak co-changes. We also extracted rules to provide insights about the metrics using classification tree. To the best of our knowledge, this is the first study that investigated social aspects to predict change dependencies and the results obtained are very promising.
KW - Conway's law
KW - change dependencies
KW - communication network
KW - social network analysis
KW - structural holes metrics
UR - http://www.scopus.com/inward/record.url?scp=84905922040&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84905922040&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-10166-8_27
DO - 10.1007/978-3-319-10166-8_27
M3 - Conference contribution
AN - SCOPUS:84905922040
SN - 9783319101651
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 294
EP - 310
BT - Collaboration and Technology - 20th International Conference, CRIWG 2014, Proceedings
PB - Springer-Verlag
T2 - 20th International Conference on Collaboration and Technology, CRIWG 2014
Y2 - 7 September 2014 through 10 September 2014
ER -