Please use this identifier to cite or link to this item: http://hdl.handle.net/1843/ESBF-B36HQW
Type: Tese de Doutorado
Title: Identifying key developers in software projects using code authorship metrics
Authors: Guilherme Amaral Avelino
First Advisor: Marco Tulio de Oliveira Valente
First Referee: Eduardo Magno Lages Figueiredo
Second Referee: Mirella Moura Moro
Third Referee: Fernando Jose Castor de Lima Filho
Abstract: Code authorship is a key information in software projects. However, its practical usage in such projects is not widely explored. Therefore, in this thesis, we first define several authorship-centric concepts, which we use to investigate the development teams of 115 open source projects, including an in-depth analysis of the Linux kernel. After, we use code authorship metrics to address two well-known software engineering problems: (1) to assess knowledge concentration in software projects and (2) to identify skilled developers to maintain specific source code files. To address the first problem, we propose an algorithm to estimate truck factors (TF), a concept widely used by practitioners to reveal key project members. We use this algorithm to detect TF events in 1,932 projects and to reveal the practices that help them to overcome such events. Finally, to address the second problem, we investigate the effectiveness of authorship metrics to identify skilled maintainers in 10 projects.
Abstract: Code authorship is a key information in software projects. However, its practical usage in such projects is not widely explored. Therefore, in this thesis, we first define several authorship-centric concepts, which we use to investigate the development teams of 115 open source projects, including an in-depth analysis of the Linux kernel. After, we use code authorship metrics to address two well-known software engineering problems: (1) to assess knowledge concentration in software projects and (2) to identify skilled developers to maintain specific source code files. To address the first problem, we propose an algorithm to estimate truck factors (TF), a concept widely used by practitioners to reveal key project members. We use this algorithm to detect TF events in 1,932 projects and to reveal the practices that help them to overcome such events. Finally, to address the second problem, we investigate the effectiveness of authorship metrics to identify skilled maintainers in 10 projects.
Subject: Code autorship
Desenvolvimento
Truck Factor
Computação
Engenharia de software
Key developers
language: Português
Publisher: Universidade Federal de Minas Gerais
Publisher Initials: UFMG
Rights: Acesso Aberto
URI: http://hdl.handle.net/1843/ESBF-B36HQW
Issue Date: 21-Jun-2018
Appears in Collections:Teses de Doutorado

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