automated analysis of load testing results
Many software systems must be load tested to ensure that they can scale up while maintaining functional and performance requirements. Current industrial practices for checking the results of a load test remain ad hoc, involving high level checks. Few research efforts are devoted to the automated analysis of load testing results, mainly due to the limited access to large scale systems for use as case studies. Automated and systematic load testing analysis is going to be much needed, as many services have been offered online to an increasing number of users. This dissertation proposes automated approaches to detect functional and performance problems in a load test by mining the recorded load testing data (execution logs and performance metrics). Case studies show that our approaches scale well to large enterprise systems and output high precision results that help analysts detect load testing problems.
load testing software mining dynamic analysis