QRTest: Automatic Query Reformulation for Information Retrieval Based Regression Test Case Prioritization

Abstract

The most effective regression testing algorithms have long running times and often require dynamic or static code analysis, making them unsuitable for the modern software development environment where the rate of software delivery could be less than a minute. More recently, some researchers have developed information retrieval-based (IR-based) techniques for prioritizing tests such that the higher similar tests to the code changes have a higher likelihood of finding bugs. A vast majority of these techniques are based on standard term similarity calculation, which can be imprecise. One reason for the low accuracy of these techniques is that the original query often is short, therefore, it does not return the relevant test cases. In such cases, the query needs reformulation. The current state of research lacks methods to increase the quality of the query in the regression testing domain. Our research aims at addressing this problem and we conjecture that enhancing the quality of the queries can improve the performance of IR-based regression test case prioritization (RTP). Our empirical evaluation with six open source programs shows that our approach improves the accuracy of IR-based RTP and increases regression fault detection rate, compared to the common prioritization techniques.