Markus Dlugi
Andreas Brunnert
ABSTRACT
In order to increase the frequency of software releases and to improve their quality, continuous integration (CI) systems became widely used in recent years. Unfortunately, it is not easy to evaluate the performance of a software release in such systems. One of the main reasons for this difficulty is often the lack of a test environment that is comparable to a production system. Performance models can help in this scenario by eliminating the need for a production-sized environment. Building upon these capabilities of performance models, we have introduced a model-based performance change detection process for continuous delivery pipelines in a previous work. This work presents an implementation of the process as plug-in for the CI system Jenkins.
- S. Becker, H. Koziolek, and R. Reussner. The palladio component model for model-driven performance prediction. Journal of Systems and Software, 82(1):3–22, 2009. Google Scholar
Digital Library
- A. Brunnert and H. Krcmar. Detecting performance change in enterprise application versions using resource profiles. In Proceedings of the 8th International Conference on Performance Evaluation Methodologies and Tools, VALUETOOLS ’14, pages 165–172, ICST, Brussels, Belgium, 2014. Google ScholarDigital Library
- A. Brunnert, C. Vögele, A. Danciu, M. Pfaff, M. Mayer, and H. Krcmar. Performance management work. Business & Information Systems Engineering, 6(3):177––179, 2014.Google ScholarCross Ref
- A. Brunnert, K. Wischer, and H. Krcmar. Using architecture-level performance models as resource profiles for enterprise applications. In Proceedings of the 10th International ACM Sigsoft Conference on Quality of Software Architectures, QoSA ’14, pages 53–62, New York, NY, USA, 2014. ACM. Google ScholarDigital Library
- J. Humble and D. Farley. Continuous Delivery: Reliable Software Releases Through Build, Test, and Deployment Automation. Addison-Wesley Professional, 1st edition, 2010. Google ScholarDigital Library
Index Terms
- Model-based performance evaluations in continuous delivery pipelines
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