Context: Blocking bugs are bugs that prevent other bugs from being fixed. Previous studies show that blocking bugs take approximately two to three times longer to be fixed compared to non-blocking bugs. Objective: Thus, automatically predicting blocking bugs early on so that developers are aware of them, can help reduce the impact of or avoid blocking bugs. However, a major challenge when predicting blocking bugs is that only a small proportion of bugs are blocking bugs, i.e., there is an unequal distribution between blocking and non-blocking bugs. For example, in Eclipse and OpenOffice, only 2.8% and 3.0% bugs are blocking bugs, respectively. We refer to this as the class imbalance phenomenon. Conclusion: ELBlocker can help deal with the class imbalance phenomenon and improve the prediction of blocking bugs. ELBlocker achieves a substantial and statistically significant improvement over the state-of-the-art methods, i.e., Garcia and Shihab’s method, SMOTE, OSS, and Bagging.
Blocking bug, Ensemble learning, Imbalance learning
Software and Cyber-Physical Systems
Information and Software Technology
XIA, Xin; David LO; SHIHAB, Emad; WANG, Xinyu; and YANG, Xiaohu.
ELBlocker: Predicting blocking bugs with ensemble imbalance learning. (2015). Information and Software Technology. 61, 93-106. Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/3100
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