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Developers' performance analysis based on code review data: How to perform comparisons of different groups of developers
Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
2016 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

Nowadays more and more IT companies switch to the distributed development model. This trend has a number of advantages and disadvantages, which are studied by researchers through different aspects of the modern code development. One of such aspects is code review, which is used by many companies and produces a big amount of data. A number of studies describe different data mining and data analysis approaches, which are based on a link between code review data and performance. According to these studies analysis of the code review data can give a good insight to the development performance and help software companies to detect a number of performance issues and improve the quality of their code.

The main goal of this Thesis was to collect reported knowledge about the code review data analysis and implement a solution, which will help to perform such analysis in a real industrial setting.

During the performance of the research the author used multiple research techniques, such as Snowballing literature review, Case study and Semi-structured interviews.

The results of the research contain a list of code review data metrics, extracted from the literature and a software tool for collecting and visualizing data.

The performed literature review showed that among the literature sources, related to the code review, relatively small amount of sources are related to the topic of the Thesis, which exposes a field for a future research. Application of the found metrics showed that most of the found metrics are possible to use in the context of the studied environment. Presentation of the results and interviews with company's representatives showed that the graphic plots are useful for observing trends and correlations in development of company's development sites and help the company to improve its performance and decision making process.

Place, publisher, year, edition, pages
2016. , 121 p.
Keyword [en]
code review, metrics, data mining
National Category
Software Engineering
Identifiers
URN: urn:nbn:se:bth-13335OAI: oai:DiVA.org:bth-13335DiVA: diva2:1044763
Subject / course
PA2534 Master's Thesis (120 credits) in Software Engineering
Educational program
PAAXA Master of Science Programme in Software Engineering
Presentation
2016-09-26, J1650, Valhallavägen, Karlskrona, 10:00 (English)
Supervisors
Examiners
Available from: 2016-12-16 Created: 2016-11-06 Last updated: 2016-12-16Bibliographically approved

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fulltext(8187 kB)123 downloads
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Type fulltextMimetype application/pdf

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