Unsupervised Outlier Detection in Software Engineering

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dc.contributor.authorLarsson, Henrik
dc.contributor.authorLindqvist, Erik
dc.contributor.departmentChalmers tekniska högskola / Institutionen för data- och informationsteknik (Chalmers)sv
dc.contributor.departmentChalmers University of Technology / Department of Computer Science and Engineering (Chalmers)en
dc.date.accessioned2019-07-03T13:39:04Z
dc.date.available2019-07-03T13:39:04Z
dc.date.issued2014
dc.description.abstractThe increasing complexity of software systems has lead to increased demands on the tools and methods used when developing software systems. To determine if a tool or method is more efficient or accurate than others empirical studies are used. The data used in empirical studies might be affected by outliers i.e. data points that deviates significantly from the rest of the data set. Hence, the statistical analysis might be distorted by these outliers as well. This study investigates if outliers are present within Empirical Software Engineering (ESE) studies using unsupervised methods for detection. It also tries to assess if the statistical analyses performed in ESE studies are affected by outliers by removing them and performing a re-analysis. The subjects used in this study comes from a narrow literature review of recently published papers within Software Engineering (SE). While collecting the samples needed for this study the current state of practise regarding data availability and analysis reproducibility is investigated. This study's results shows that outliers can be found in ESE studies and it also identifies issues regarding data availability within the same field. Finally, this study presents guidelines for how to improve the way outlier detection is presented within ESE studies as well as guidelines for publishing data.
dc.identifier.urihttps://hdl.handle.net/20.500.12380/216772
dc.language.isoeng
dc.setspec.uppsokTechnology
dc.subjectData- och informationsvetenskap
dc.subjectComputer and Information Science
dc.titleUnsupervised Outlier Detection in Software Engineering
dc.type.degreeExamensarbete för masterexamensv
dc.type.degreeMaster Thesisen
dc.type.uppsokH
local.programmeSoftware engineering and technology (MPSOF), MSc
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