An adaptive bayesian approach for URL selection to test performance of large scale web-based systems
Alim Ul Gias, Kazi Muheymin Sakib · 2014
In case of large scale web-based systems, scripts for performance testing are updated iteratively. In each script, multiple URLs of the system are considered depending on intuitions that those URLs will expose the performance bugs. This paper proposes a Bayesian approach for including a URL to a test script based on its probability of being time intensive. As the testing goes on the scheme adaptively updates its knowledge regarding a URL. The comparison with existing methods shows that the proposed technique performs similar in guiding applications towards intensive tasks, which helps to expose performance bugs.