Mining GitHub Issues for Bugs, Feature Requests and Questions

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dc.contributor.authorJokhio, Marvi
dc.contributor.supervisorErnst, Neil A.
dc.date.accessioned2021-12-15T02:37:14Z
dc.date.available2021-12-15T02:37:14Z
dc.date.copyright2021en_US
dc.date.issued2021-12-14
dc.degree.departmentDepartment of Computer Scienceen_US
dc.degree.levelMaster of Science M.Sc.en_US
dc.description.abstractThe maintenance and success of software projects highly depend on updated and bug-free code. To effectively process hundreds of daily new issues in big software projects, tools like issue tracking systems (ITS) play an important role but the critical aspect for issue processing and triaging needs assignment of accurate labels to determine their type (e.g., bug, feature, question and so on). This labelling is a time-consuming and tedious task and hence needs automated solutions. Automatic classification of issues is a challenging task due to semantically ambiguous text which contains code, links, package and method names, commands etc. In this work, we propose supervised and unsupervised mining techniques for GitHub issues using text only. In the supervised machine learning technique, we show that our model can classify issues in the bug, feature, and question classes with 86.7% AUC scores. We also proposed a technique to extract topics from GitHub issues using Latent Dirichlet Allocation (LDA) to analyze the type of development issues faced by developers.en_US
dc.description.scholarlevelGraduateen_US
dc.identifier.urihttp://hdl.handle.net/1828/13592
dc.language.isoenen_US
dc.rightsAvailable to the World Wide Weben_US
dc.subjectGitHub Issuesen_US
dc.subjectTopic Modelingen_US
dc.subjectMachine Learningen_US
dc.subjectSoftware Bugsen_US
dc.subjectSoftware Feature Requestsen_US
dc.subjectText Miningen_US
dc.titleMining GitHub Issues for Bugs, Feature Requests and Questionsen_US
dc.typeprojecten_US

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