Recommending frequently encountered bugs

• Main Authors: ZHANG, Yun, LO, David, XIA, Xin, JIANG, Jing, SUN, Jianling
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2018
• Subjects: Software Engineering
• Online Access: https://ink.library.smu.edu.sg/sis_research/4291; https://ink.library.smu.edu.sg/context/sis_research/article/5294/viewcontent/icpc183.pdf
• Institution: Singapore Management University

Developers introduce bugs during software development which reduce software reliability. Many of these bugs are commonly occurring and have been experienced by many other developers. Informingdevelopers, especially novice ones, about commonly occurring bugsin a domain of interest (e.g., Java), can help developers comprehendprogram and avoid similar bugs in the future. Unfortunately, information about commonly occurring bugs are not readily available. Toaddress this need, we propose a novel approach named RFEB whichrecommends frequently encountered bugs (FEBugs) that may affectmany other developers. RFEB analyzes Stack Overflow which is thelargest software engineering-specific Q&A communities. Amongthe plenty of questions posted in Stack Overflow, many of themprovide the descriptions and solutions of different kinds of bugs.Unfortunately, the search engine that comes with Stack Overflow isnot able to identify FEBugs well. To address the limitation of thesearch engine of Stack Overflow, we propose RFEB which is anintegrated and iterative approach that considers both relevance andpopularity of Stack Overflow questions to identify FEBugs. To evaluate the performance of RFEB, we perform experiments on a datasetfrom Stack Overflow which contains more than ten million posts.We compared our model with Stack Overflow’s search engine on 10domains, and the experiment results show that RFEB achieves theaverage ����10 score of 0.96, which improves Stack Overflow’ssearch engine by 20%.