ELBlocker: Predicting blocking bugs with ensemble imbalance learning

• Main Authors: XIA, Xin, David LO, SHIHAB, Emad, WANG, Xinyu, YANG, Xiaohu
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2015
• Subjects: Blocking bug; Ensemble learning; Imbalance learning; Software Engineering
• Online Access: https://ink.library.smu.edu.sg/sis_research/3100; https://ink.library.smu.edu.sg/context/sis_research/article/4100/viewcontent/ELBlockerPredictingBlockingBugs_2015.pdf
• Institution: Singapore Management University

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.