A Framework to Evaluate the Effectiveness of Different Load Testing Analysis Techniques

Abstract

Large-scale software systems like Amazon and eBay must be load tested to ensure they can handle hundreds and millions of current requests in the field. Load testing usually lasts for a few hours or even days and generates large volumes of system behavior data (execution logs and counters). This data must be properly analyzed to check whether there are any performance problems in a load test. However, the sheer size of the data prevents effective manual analysis. In addition, unlike functional tests, there is usually no test oracle associated with a load test. To cope with these challenges, there have been many analysis techniques proposed to automatically detect problems in a load test by comparing the behavior of the current test against previous test(s). Unfortunately, none of these techniques compare their performance against each other. In this paper, we have proposed a framework, which evaluates and compares the effectiveness of different test analysis techniques. We have evaluated a total of 23 test analysis techniques using load testing data from three open source systems. Based on our experiments, we have found that all the test analysis techniques can effectively build performance models using data from both buggy or non-buggy tests and flag the performance deviations between them. It is more cost-effective to compare the current test against two recent previous test(s), while using testing data collected under longer sampling intervals (≥180 seconds). Among all the test analysis techniques, Control Chart, Descriptive Statistics and Regression Tree yield the best performance. Our evaluation framework and findings can be very useful for load testing practitioners and researchers. To encourage further research on this topic, we have made our testing data publicity available to download.