A survey on machine learning-based automated software bug report classification

buir.contributor.authorÖztürk, Ceyhun Emre
buir.contributor.orcidÖztürk, Ceyhun Emre|0000-0001-9744-6778
dc.citation.epage640en_US
dc.citation.spage635en_US
dc.contributor.authorKöksal, Ö.
dc.contributor.authorÖztürk, Ceyhun Emre
dc.coverage.spatialAnkara, Turkeyen_US
dc.date.accessioned2023-02-28T08:28:09Z
dc.date.available2023-02-28T08:28:09Z
dc.date.issued2022-11-14
dc.departmentDepartment of Electrical and Electronics Engineeringen_US
dc.descriptionConference Name: International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT)en_US
dc.descriptionDate of Conference: 20-22 October 2022en_US
dc.description.abstractIn software development processes, classifying software bugs is a vital step since it helps grasp the nature, implications, and causes of software failures. Further, categorization enables reacting to software bugs appropriately and faster. However, manual classification of software bugs is inefficient and costly, especially in large-scale software projects, since one must deal with extensive bug reports from multiple sources. Hence, many studies have addressed this problem by automated software bug classification with the help of machine learning techniques. Researchers used various machine learning-based algorithms and techniques to obtain better classification performance. Furthermore, many researchers used open source bug repositories to compare their results with previous studies. In this paper, we aimed to report the main studies in machine learning-based automated software bug report classification by highlighting the recent improvements and indicating the key steps in this process. So, this survey can benefit the researchers and practitioners working in automated software bug report classification and other related domains.en_US
dc.identifier.doi10.1109/ISMSIT56059.2022.9932822en_US
dc.identifier.urihttp://hdl.handle.net/11693/111890
dc.language.isoEnglishen_US
dc.publisherInstitute of Electrical and Electronics Engineersen_US
dc.relation.isversionofhttps://doi.org/10.1109/ISMSIT56059.2022.9932822en_US
dc.source.titleInternational Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT)en_US
dc.subjectSoftware engineeringen_US
dc.subjectSoftware bug report classificationen_US
dc.subjectMachine learningen_US
dc.subjectDeep learningen_US
dc.subjectNatural language processingen_US
dc.titleA survey on machine learning-based automated software bug report classificationen_US
dc.typeConference Paperen_US
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