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Tuning of machine learning algorithms for automatic bug assignment
Linköping University, Department of Computer and Information Science, Software and Systems.
2017 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

In software development projects, bug triage consists mainly of assigning bug reports to software developers or teams (depending on the project). The partial or total automation of this task would have a positive economic impact on many software projects. This thesis introduces a systematic four-step method to find some of the best configurations of several machine learning algorithms intending to solve the automatic bug assignment problem. These four steps are respectively used to select a combination of pre-processing techniques, a bug report representation, a potential feature selection technique and to tune several classifiers. The aforementioned method has been applied on three software projects: 66 066 bug reports of a proprietary project, 24 450 bug reports of Eclipse JDT and 30 358 bug reports of Mozilla Firefox. 619 configurations have been applied and compared on each of these three projects. In production, using the approach introduced in this work on the bug reports of the proprietary project would have increased the accuracy by up to 16.64 percentage points.

Place, publisher, year, edition, pages
2017. , 135 p.
Keyword [en]
bug triage, bug assignment, bug mining, bug report, activity-based approach, issue tracking, bug repository, bug tracker, pre-processing, feature extraction, feature selection, tuning, model selection, hyper-parameter optimization, text mining, text classification, classifier, supervised learning, machine learning, information retrieval, bugzilla, eclipse jdt, mozilla firefox, open source software, proprietary project, accuracy, mean reciprocal rank, software development, software maintenance, software engineering
National Category
Computer and Information Science
Identifiers
URN: urn:nbn:se:liu:diva-139230ISRN: LIU-IDA/LITH-EX-A--17/022--SEOAI: oai:DiVA.org:liu-139230DiVA: diva2:1120657
Subject / course
Computer science
Presentation
2017-06-07, Alan Turing, Building E, First Floor (Level 3), Campus Valla, Linköping, 10:30 (English)
Supervisors
Examiners
Available from: 2017-07-07 Created: 2017-07-06 Last updated: 2017-07-07Bibliographically approved

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CiteExportLink to record
Permanent link

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Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
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