Unsupervised Outlier Detection in Software Engineering

Examensarbete för masterexamen

Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.12380/216772
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Bibliographical item details
Type: Examensarbete för masterexamen
Master Thesis
Title: Unsupervised Outlier Detection in Software Engineering
Authors: Larsson, Henrik
Lindqvist, Erik
Abstract: The 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.
Keywords: Data- och informationsvetenskap;Computer and Information Science
Issue Date: 2014
Publisher: Chalmers tekniska högskola / Institutionen för data- och informationsteknik (Chalmers)
Chalmers University of Technology / Department of Computer Science and Engineering (Chalmers)
URI: https://hdl.handle.net/20.500.12380/216772
Collection:Examensarbeten för masterexamen // Master Theses

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