Knowledge Systems Institute Graduate School (Skokie, IL, United States)
Abstract
Test case prioritization aims to schedule test cases in a certain order such that the effectiveness of regression testing can be improved. Prioritization using random sequence is a basic and simple technique, and normally acts as a benchmark to evaluate other prioritization techniques. Adaptive Random Sequence (ARS) makes use of extra information to improve the diversity of random sequence. Some researchers have proposed prioritization techniques using ARS with white-box code coverage information that is normally related to the test execution history of previous versions. In this paper, we propose several ARS-based prioritization techniques using black-box information. The proposed techniques schedule test cases based on the string distances of the input data, without referring to the execution history. Our experimental studies show that these new techniques deliver higher fault-detection effectiveness than random prioritization. In addition, as compared with an existing blackbox prioritization technique, the new techniques have similar fault-detection effectiveness but much lower computation overhead, and thus are more cost-effective
