TY - GEN
T1 - Predição da participação de desenvolvedores em tarefas em projetos de software livre
AU - Schwerz, André Luis
AU - Liberato, Rafael
AU - Wiese, Igor Scaliante
AU - Steinmacher, Igor
AU - Gerosa, Marco Aurélio
AU - Ferreira, João Eduardo
PY - 2012
Y1 - 2012
N2 - Developers of distributed open source projects use management and issues tracking tool to communicate. These tools provide a large volume of unstructured information that makes the triage of issues difficult, increasing developers' overhead. This problem is common to online communities based on volunteer participation. This paper shows the importance of the content of comments in an open source project to build a classifier to predict the participation for a developer in an issue. To design this prediction model, we used two machine learning algorithms called Naive Bayes and J48. We used the data of three Apache Hadoop subprojects to evaluate the use of the algorithms. By applying our approach to the most active developers of these subprojects we have achieved an accuracy ranging from 79% to 96%. The results indicate that the content of comments in issues of open source projects is a relevant factor to build a classifier of issues for developers.
AB - Developers of distributed open source projects use management and issues tracking tool to communicate. These tools provide a large volume of unstructured information that makes the triage of issues difficult, increasing developers' overhead. This problem is common to online communities based on volunteer participation. This paper shows the importance of the content of comments in an open source project to build a classifier to predict the participation for a developer in an issue. To design this prediction model, we used two machine learning algorithms called Naive Bayes and J48. We used the data of three Apache Hadoop subprojects to evaluate the use of the algorithms. By applying our approach to the most active developers of these subprojects we have achieved an accuracy ranging from 79% to 96%. The results indicate that the content of comments in issues of open source projects is a relevant factor to build a classifier of issues for developers.
KW - Content analysis
KW - issue tracking classifier
KW - machine learning
KW - prediction model
UR - http://www.scopus.com/inward/record.url?scp=84872517468&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84872517468&partnerID=8YFLogxK
U2 - 10.1109/SBSC.2012.27
DO - 10.1109/SBSC.2012.27
M3 - Conference contribution
AN - SCOPUS:84872517468
SN - 9780769548906
T3 - Proceedings - 9th Brazilian Symposium on Collaborative Systems, SBSC 2012
SP - 109
EP - 114
BT - Proceedings - 9th Brazilian Symposium on Collaborative Systems, SBSC 2012
T2 - 9th Brazilian Symposium on Collaborative Systems, SBSC 2012
Y2 - 15 October 2012 through 18 October 2012
ER -