Automated identification of high impact bug reports leveraging imbalanced learning strategies

• Main Authors: YANG, Xinli, David LO, HUANG, Qiao, XIA, Xin, SUN, Jianling
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2016
• Subjects: High Impact Bug; Imbalanced Data; Text Classification; Computer Sciences; Software Engineering
• Online Access: https://ink.library.smu.edu.sg/sis_research/3567; https://ink.library.smu.edu.sg/context/sis_research/article/4568/viewcontent/AutomatedIDHighImpactBugReportsLimbalLearning_2016.pdf
• Institution: Singapore Management University

In practice, some bugs have more impact than others and thus deserve more immediate attention. Due to tight schedule and limited human resource, developers may not have enough time to inspect all bugs. Thus, they often concentrate on bugs that are highly impactful. In the literature, high impact bug...