TY - JOUR
T1 - Where should we fix this bug? A two-phase recommendation model
AU - Kim, Dongsun
AU - Tao, Yida
AU - Kim, Sunghun
AU - Zeller, Andreas
PY - 2013
Y1 - 2013
N2 - To support developers in debugging and locating bugs, we propose a two-phase prediction model that uses bug reports' contents to suggest the files likely to be fixed. In the first phase, our model checks whether the given bug report contains sufficient information for prediction. If so, the model proceeds to predict files to be fixed, based on the content of the bug report. In other words, our two-phase model "speaks up" only if it is confident of making a suggestion for the given bug report; otherwise, it remains silent. In the evaluation on the Mozilla "Firefox" and "Core" packages, the two-phase model was able to make predictions for almost half of all bug reports; on average, 70 percent of these predictions pointed to the correct files. In addition, we compared the two-phase model with three other prediction models: the Usual Suspects, the one-phase model, and BugScout. The two-phase model manifests the best prediction performance.
AB - To support developers in debugging and locating bugs, we propose a two-phase prediction model that uses bug reports' contents to suggest the files likely to be fixed. In the first phase, our model checks whether the given bug report contains sufficient information for prediction. If so, the model proceeds to predict files to be fixed, based on the content of the bug report. In other words, our two-phase model "speaks up" only if it is confident of making a suggestion for the given bug report; otherwise, it remains silent. In the evaluation on the Mozilla "Firefox" and "Core" packages, the two-phase model was able to make predictions for almost half of all bug reports; on average, 70 percent of these predictions pointed to the correct files. In addition, we compared the two-phase model with three other prediction models: the Usual Suspects, the one-phase model, and BugScout. The two-phase model manifests the best prediction performance.
KW - Bug reports
KW - Machine learning
KW - Patch file prediction
UR - http://www.scopus.com/inward/record.url?scp=84887856685&partnerID=8YFLogxK
U2 - 10.1109/TSE.2013.24
DO - 10.1109/TSE.2013.24
M3 - Article
AN - SCOPUS:84887856685
SN - 0098-5589
VL - 39
SP - 1597
EP - 1610
JO - IEEE Transactions on Software Engineering
JF - IEEE Transactions on Software Engineering
IS - 11
M1 - 6517844
ER -