Legion: Massively composing rankers for improved bug localization at Adobe

• Main Authors: JARMAN, Darryl, BERRY, Jeffrey, SMITH, Riley, THUNG, Ferdian, LO, David
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2022
• Subjects: Bug localization; information retrieval; bug reports; data augmentation; ranker composition; industrial study; Software Engineering
• Online Access: https://ink.library.smu.edu.sg/sis_research/7636; https://ink.library.smu.edu.sg/context/sis_research/article/8639/viewcontent/09415126.pdf
• Institution: Singapore Management University

Studies have estimated that, in industrial settings, developers spend between 30 and 90 percent of their time fixing bugs. As such, tools that assist in identifying the location of bugs provide value by reducing debugging costs. One such tool is BugLocator. This study initially aimed to determine if...