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Erschienen in: Software Quality Journal 4/2019

29.08.2019

Algorithms for estimating truck factors: a comparative study

verfasst von: Mívian Ferreira, Thaís Mombach, Marco Tulio Valente, Kecia Ferreira

Erschienen in: Software Quality Journal | Ausgabe 4/2019

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Abstract

Software development is a knowledge-intensive industry. For this reason, concentration of knowledge in software projects tends to be very risky, which increases the relevance of strategies that reveal how source code knowledge is distributed among team members. The truck factor (also known as the bus factor) is an increasingly popular concept—proposed by practitioners—that indicates the minimal number of developers that have to be hit by a truck (or leave the team) before a project is incapacitated. Therefore, it is a measure that reveals the concentration of knowledge and the key developers in a project. Due to the importance of this concept, algorithms have been proposed to automatically compute truck factors, using maintenance activity data extracted from version control systems. However, we still lack large studies that assess the results of truck factor algorithms. To fulfill this gap in the literature, this paper describes the results of three empirical studies. In the first study, we validate the results produced by three algorithms to estimate truck factors. To this purpose, we build an oracle of truck factors, gathered via a survey with 35 open-source project teams. In the second study, we provide a comparison between truck factors and core developers, a related concept commonly used to denote the key developers of open-source projects. Our results indicate that truck factor developers are in most cases a subset of core developers. Finally, as the algorithms proposed so far are based in commit data, in the third study, we investigate other factors that may impact the computation of truck factors.

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Fußnoten
1
 
7
We also ran experiments for 100,000 samples. After increasing the number of analyzed samples, the results presented by the RIG algorithm did not show significant improvements: 13 systems presented error = 0, and 21 systems have |error| ≤ 1.
 
8
Some answers mentioned more than one role, so the sum exceeds 100%.
 
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Metadaten
Titel
Algorithms for estimating truck factors: a comparative study
verfasst von
Mívian Ferreira
Thaís Mombach
Marco Tulio Valente
Kecia Ferreira
Publikationsdatum
29.08.2019
Verlag
Springer US
Erschienen in
Software Quality Journal / Ausgabe 4/2019
Print ISSN: 0963-9314
Elektronische ISSN: 1573-1367
DOI
https://doi.org/10.1007/s11219-019-09457-2

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