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1	Tuning of machine learning algorithms for automatic bug assignment Artchounin, Daniel January 2017 (has links) 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. 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 Computer and Information Sciences Data- och informationsvetenskap

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Tuning of machine learning algorithms for automatic bug assignment