Scalability testing automation using multivariate characterization and detection of software performance antipatterns

Software Performance Antipatterns ( in large scale systems.In this paper, we extended our previously proposed approach for the automated characterization and detection designed to support continuous integration/delivery/deployment (CI/CDD) pipelines, with the goal of addressing the lack of computationally efficient algorithms.We introduce a machine learning-based approach to improve the detection of and interpretation of approach’s results. The approach is complemented with a simulation-based methodology to analyze different architectural alternatives and measure the precision and recall of our approach. Our approach includes statistical characterization using a multivariate analysis of load testing experimental results to identify the services that have the largest impact on system scalability.To show the effectiveness of our approach, we have applied it to a large complex telecom system at Ericsson. We have built a simulation model of the Ericsson system and we have evaluated the introduced methodology by using simulation-based .We contributed to the state-of-the-art by introducing a novel approach to support computationally efficient characterization and detection that has been applied to a large complex system using performance testing data. We have compared the computational efficiency of the proposed approach with state-of-the-art heuristics. We have found that the approach introduced in this paper grows linearly, which is a significant improvement over existing techniques.