TY - GEN
T1 - A hybrid bug triage algorithm for developer recommendation
AU - Zhang, Tao
AU - Lee, Byungjeong
PY - 2013
Y1 - 2013
N2 - With a great number of software applications that have been developed, software maintenance has become an important and challenging task, particularly due to the increasing scale of software projects. Even if developers can create and update bug reports in bug repositories to support software maintenance, a large software project receives a large number of bug reports each day. For reducing the workload of developers, many researchers and software engineers have begun recommending appropriate developers to fix bugs. This process is called bug triage and is a hot research topic for software maintenance. In this paper, we propose a hybrid bug triage algorithm, combining a probability model and an experience model to rank all candidate developers for fixing a new bug. For this study, we adopted the smoothed Unigram Model (UM) instead of the traditional Vector Space Model (VSM) to search similar bug reports. In the probability model, we used a social network to analyze the probability of fixing a new bug for a candidate developer. We first proposed to add a new feature (the number of re-opened bugs) in order to get the fixing probability. In the experience model, we considered the number of fixed bugs and fixing cost for each candidate developer as the estimate factor. In addition, we introduced a new concept, activity factor, to better model developers' experience. We performed the experiments on two large-scale, open source projects. The results show that our method can effectively recommend the best developer for fixing bugs.
AB - With a great number of software applications that have been developed, software maintenance has become an important and challenging task, particularly due to the increasing scale of software projects. Even if developers can create and update bug reports in bug repositories to support software maintenance, a large software project receives a large number of bug reports each day. For reducing the workload of developers, many researchers and software engineers have begun recommending appropriate developers to fix bugs. This process is called bug triage and is a hot research topic for software maintenance. In this paper, we propose a hybrid bug triage algorithm, combining a probability model and an experience model to rank all candidate developers for fixing a new bug. For this study, we adopted the smoothed Unigram Model (UM) instead of the traditional Vector Space Model (VSM) to search similar bug reports. In the probability model, we used a social network to analyze the probability of fixing a new bug for a candidate developer. We first proposed to add a new feature (the number of re-opened bugs) in order to get the fixing probability. In the experience model, we considered the number of fixed bugs and fixing cost for each candidate developer as the estimate factor. In addition, we introduced a new concept, activity factor, to better model developers' experience. We performed the experiments on two large-scale, open source projects. The results show that our method can effectively recommend the best developer for fixing bugs.
KW - Bug triage
KW - Experience model
KW - Probability model
KW - Smoothed Unigram Model
KW - Social network
UR - http://www.scopus.com/inward/record.url?scp=84877978382&partnerID=8YFLogxK
U2 - 10.1145/2480362.2480568
DO - 10.1145/2480362.2480568
M3 - Conference contribution
AN - SCOPUS:84877978382
SN - 9781450316569
T3 - Proceedings of the ACM Symposium on Applied Computing
SP - 1088
EP - 1094
BT - 28th Annual ACM Symposium on Applied Computing, SAC 2013
T2 - 28th Annual ACM Symposium on Applied Computing, SAC 2013
Y2 - 18 March 2013 through 22 March 2013
ER -