Beyond Technical Aspects

How Do Community Smells Influence the Intensity of Code Smells?

Journal Article (2021)
Author(s)

Fabio Palomba (Universitat Zurich)

Damian Tamburri (Eindhoven University of Technology)

Francesca Arcelli Fontana (University of Milano-Bicocca)

Rocco Oliveto (University of Molise)

A.E. Zaidman (TU Delft - Software Engineering)

Alexander Serebrenik (Eindhoven University of Technology)

Research Group
Software Engineering
Copyright
© 2021 Fabio Palomba, Damian Andrew Tamburri, Francesca Arcelli Fontana, Rocco Oliveto, A.E. Zaidman, Alexander Serebrenik
DOI related publication
https://doi.org/10.1109/TSE.2018.2883603
More Info
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Publication Year
2021
Language
English
Copyright
© 2021 Fabio Palomba, Damian Andrew Tamburri, Francesca Arcelli Fontana, Rocco Oliveto, A.E. Zaidman, Alexander Serebrenik
Research Group
Software Engineering
Issue number
1
Volume number
47
Pages (from-to)
108-129
Reuse Rights

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Abstract

Code smells are poor implementation choices applied by developers during software evolution that often lead to critical flaws or failure. Much in the same way, community smells reflect the presence of organizational and socio-Technical issues within a software community that may lead to additional project costs. Recent empirical studies provide evidence that community smells are often-if not always-connected to circumstances such as code smells. In this paper we look deeper into this connection by conducting a mixed-methods empirical study of 117 releases from 9 open-source systems. The qualitative and quantitative sides of our mixed-methods study were run in parallel and assume a mutually-confirmative connotation. On the one hand, we survey 162 developers of the 9 considered systems to investigate whether developers perceive relationship between community smells and the code smells found in those projects. On the other hand, we perform a fine-grained analysis into the 117 releases of our dataset to measure the extent to which community smells impact code smell intensity (i.e., criticality). We then propose a code smell intensity prediction model that relies on both technical and community-related aspects. The results of both sides of our mixed-methods study lead to one conclusion: community-related factors contribute to the intensity of code smells. This conclusion supports the joint use of community and code smells detection as a mechanism for the joint management of technical and social problems around software development communities.

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