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Final Thesis: An Exploratory Data Analysis of Code Review Data

Abstract: Modern code review enables developers to conduct light weighted inspections using tools and is a well-established part of the software development process. However, it is not clearly defined if current processes and methods are done efficiently. In this paper, we examined a dataset from a tool supported code review process provided by a multinational software development company to determine and test indicators for code review quality. Within an exploratory approach, inspecting data from over 250 000 entries in 5 years of reviewing practice, we checked dataset characteristics, reviewer workload, reviewer selection and social network metrics. We found evidence in all revised categories that the company lacks in an efficient code reviewing process. Our results prove that current code review practice needs better defined standards and with usage of our indicators and numbers, future studies can compare their observed code inspection performance.

Keywords: modern code review, explorative data analysis, quality assurance, inner source

PDFs: Master Thesis, Work Description

Reference: Leonard Keidel. Exploratory Data Analysis on Code Review Data. Master Thesis, Friedrich-Alexander-Universität Erlangen-Nürnberg: 2018.

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