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dc.contributor.authorOliveira Sousa, Anderson 19:24:06 (GMT) 19:24:06 (GMT)
dc.description.abstractPerformance testing is an essential part of the development life cycle that must be done in a timely fashion. However, checking for performance regressions in software can be time-consuming, especially for complex systems containing multiple lengthy tests cases. The first part of this thesis presents a technique to performance test selection using machine learning. In our approach, we build features using information extracted from the previous software versions to train classifiers that assist developers in deciding whether or not to execute a performance test on a new version. Our results show that the classifiers can be used as a mechanism that aids test selection and consequently avoids unnecessary testing. The second part of this work investigates the binning effect on user-space memory allocators. First, we examine how binning events can be a source of performance outliers in Redis and CPython object allocators. Second, we implement a \textit{Pintool} to detect the occurrence of binning on Python programs. The tool performs dynamic binary instrumentation on the interpreter and outputs information that helps developers in performing code optimizations. Finally, we use our tool to investigate the presence of binning in various widely used Python libraries.en
dc.publisherUniversity of Waterlooen
dc.subjectperformance evaluationen
dc.subjectperformance test selectionen
dc.subjectbinning effecten
dc.subjectmachine learningen
dc.subjectperformance test prioritisationen
dc.subjectperformance variabilityen
dc.titlePerformance Test Selection Using Machine Learning and a Study of Binning Effect in Memory Allocatorsen
dc.typeMaster Thesisen
dc.pendingfalse and Computer Engineeringen and Computer Engineeringen of Waterlooen
uws-etd.degreeMaster of Applied Scienceen
uws.contributor.advisorFischmeister, Sebastian
uws.contributor.affiliation1Faculty of Engineeringen

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