Context: The performance assessment of complex software systems is not a trivial task since it depends on the design, code, and execution environment. All these factors may affect the system quality and generate negative consequences, such as delays and system failures. The identification of bad practices leading to performance flaws is of key relevance to avoid expensive rework in redesign, reimplementation, and redeployment. Objective: The goal of this manuscript is to provide a systematic process, based on load testing and profiling data, to identify performance issues with runtime data. These performance issues represent an important source of knowledge as they are used to trigger the software refactoring process. Software characteristics and performance measurements are matched with well-known performance antipatterns to document common performance issues and their solutions. Method: We execute load testing based on the characteristics of collected operational profile, thus to produce representative workloads. Performance data from the system under test is collected using a profiler tool to create profiler snapshots and get performance hotspot reports. From such data, performance issues are identified and matched with the specification of antipatterns. Software refactorings are then applied to solve these performance antipatterns. Results: The approach has been applied to a real-world industrial case study and to a representative laboratory study. Experimental results demonstrate the effectiveness of our tool-supported approach that is able to automatically detect two performance antipatterns by exploiting the knowledge of domain experts. In addition, the software refactoring process achieves a significant performance gain at the operational stage in both case studies. Conclusion: Performance antipatterns can be used to effectively support the identification of performance issues from load testing and profiling data. The detection process triggers an antipattern-based software refactoring that in our two case studies results in a substantial performance improvement

Exploiting load testing and profiling for Performance Antipattern Detection

Trubiani C;
2018

Abstract

Context: The performance assessment of complex software systems is not a trivial task since it depends on the design, code, and execution environment. All these factors may affect the system quality and generate negative consequences, such as delays and system failures. The identification of bad practices leading to performance flaws is of key relevance to avoid expensive rework in redesign, reimplementation, and redeployment. Objective: The goal of this manuscript is to provide a systematic process, based on load testing and profiling data, to identify performance issues with runtime data. These performance issues represent an important source of knowledge as they are used to trigger the software refactoring process. Software characteristics and performance measurements are matched with well-known performance antipatterns to document common performance issues and their solutions. Method: We execute load testing based on the characteristics of collected operational profile, thus to produce representative workloads. Performance data from the system under test is collected using a profiler tool to create profiler snapshots and get performance hotspot reports. From such data, performance issues are identified and matched with the specification of antipatterns. Software refactorings are then applied to solve these performance antipatterns. Results: The approach has been applied to a real-world industrial case study and to a representative laboratory study. Experimental results demonstrate the effectiveness of our tool-supported approach that is able to automatically detect two performance antipatterns by exploiting the knowledge of domain experts. In addition, the software refactoring process achieves a significant performance gain at the operational stage in both case studies. Conclusion: Performance antipatterns can be used to effectively support the identification of performance issues from load testing and profiling data. The detection process triggers an antipattern-based software refactoring that in our two case studies results in a substantial performance improvement
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/20.500.12571/7431
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