Configuration Smells in Continuous Delivery Pipelines: A Linter and A Six-Month Study on GitLab
| Autor: | Harald C. Gall, Massimiliano Di Penta, Sebastian Proksch, Anna Jancso, Carmine Vassallo |
|---|---|
| Přispěvatelé: | University of Zurich |
| Rok vydání: | 2020 |
| Předmět: |
Anti-patterns
10009 Department of Informatics Computer science Continuous Integration Continuous delivery 02 engineering and technology 000 Computer science knowledge & systems computer.software_genre Software 020204 information systems Server 0202 electrical engineering electronic engineering information engineering DevOps Linter Database business.industry 020207 software engineering Pipeline (software) 1712 Software Pipeline transport Anti-pattern GitLab Precision and recall business Configuration computer Continuous Delivery |
| Zdroj: | ESEC/SIGSOFT FSE |
| DOI: | 10.5281/zenodo.4024796 |
| Popis: | An effective and efficient application of Continuous Integration (CI) and Delivery (CD) requires software projects to follow certain principles and good practices. Configuring such a CI/CD pipeline is challenging and error-prone. Therefore, automated linters have been proposed to detect errors in the pipeline. While existing linters identify syntactic errors, detect security vulnerabilities or misuse of the features provided by build servers, they do not support developers that want to prevent common misconfigurations of a CD pipeline that potentially violate CD principles ("CD smells"). To this end, we propose CD-Linter, a semantic linter that can automatically identify four different smells in such a pipeline configuration file. We have evaluated our approach through a large-scale and long-term study that consists of (i) monitoring 145 issues (opened in as many open-source projects) over a period of 6 months, (ii) manually validating the detection precision and recall on a representative sample of issues, and (iii) assessing the magnitude of the observed smells on 5,312 open-source projects on GitLab. Our results show that CD smells are accepted and fixed by most of the developers and our linter achieves a precision of 87% and a recall of 94%. Those smells can be frequently observed in the wild, as 31% of projects with long configurations are affected by at least one smell. Data and Material To appear in the proceedings of the ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE), Sun 8 - Fri 13 November 2020 Sacramento, California, United States. |
| Databáze: | OpenAIRE |
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