TY - GEN
T1 - Patch reviewer recommendation in OSS projects
AU - Lee, John Boaz
AU - Ihara, Akinori
AU - Monden, Akito
AU - Matsumoto, Ken Ichi
N1 - Publisher Copyright:
© 2013 IEEE.
PY - 2013
Y1 - 2013
N2 - In an Open Source Software (OSS) project, many developers contribute by submitting source code patches. To maintain the quality of the code, certain experienced developers review each patch before it can be applied or committed. Ideally, within a short amount of time after its submission, a patch is assigned to a reviewer and reviewed. In the real world, however, many large and active OSS projects evolve at a rapid pace and the core developers can get swamped with a large number of patches to review. Furthermore, since these core members may not always be available or may choose to leave the project, it can be challenging, at times, to find a good reviewer for a patch. In this paper, we propose a graph-based method to automatically recommend the most suitable reviewers for a patch. To evaluate our method, we conducted experiments to predict the developers who will apply new changes to the source code in the Eclipse project. Our method achieved an average recall of 0.84 for top-5 predictions and a recall of 0.94 for top-10 predictions.
AB - In an Open Source Software (OSS) project, many developers contribute by submitting source code patches. To maintain the quality of the code, certain experienced developers review each patch before it can be applied or committed. Ideally, within a short amount of time after its submission, a patch is assigned to a reviewer and reviewed. In the real world, however, many large and active OSS projects evolve at a rapid pace and the core developers can get swamped with a large number of patches to review. Furthermore, since these core members may not always be available or may choose to leave the project, it can be challenging, at times, to find a good reviewer for a patch. In this paper, we propose a graph-based method to automatically recommend the most suitable reviewers for a patch. To evaluate our method, we conducted experiments to predict the developers who will apply new changes to the source code in the Eclipse project. Our method achieved an average recall of 0.84 for top-5 predictions and a recall of 0.94 for top-10 predictions.
KW - CVS
KW - Mining software repositories
KW - Patch reviewer recommendation
KW - Random walk
UR - http://www.scopus.com/inward/record.url?scp=84897469909&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84897469909&partnerID=8YFLogxK
U2 - 10.1109/APSEC.2013.103
DO - 10.1109/APSEC.2013.103
M3 - Conference contribution
AN - SCOPUS:84897469909
SN - 9780769549224
T3 - Proceedings - Asia-Pacific Software Engineering Conference, APSEC
SP - 1
EP - 6
BT - APSEC 2013 - Proceedings of the 20th Asia-Pacific Software Engineering Conference
A2 - Muenchaisri, Pornsiri
A2 - Rothermel, Gregg
PB - IEEE Computer Society
T2 - 20th Asia-Pacific Software Engineering Conference, APSEC 2013
Y2 - 2 December 2013 through 5 December 2013
ER -