Automatic detection of performance deviations in the load testing of large scale systems
Proceedings of the 2013 International Conference on Software Engineering
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Load testing is an important phase in the software development process. It is very time consuming but there is usually little time for it. As a solution to the tight testing schedule, software companies automate their testing procedures. However, existing automation only reduces the time required to run load tests. The analysis of the test results is still performed manually. A typical load test outputs thousands of performance counters. Analyzing these counters manually requires time and tacit knowledge of the system-under-test from the performance engineers. The goal of this study is to derive an approach to automatically verify load tests' results. We propose an approach based on a statistical quality control technique called control charts. Our approach can a) automatically determine if a test run passes or fails and b) identify the subsystem where performance problem originated. We conduct two case studies on a large commercial telecommunication software and an open-source software system to evaluate our approach. Our results warrant further development of control chart based techniques in performance verification.