
Test case prioritization (TCP) is a practical activity in software testing for exposing faults earlier. Researchers have proposed many TCP techniques to reorder test cases. Among them, coverage-based TCPs have been widely investigated. Specifically, coverage-based TCP approaches leverage coverage information between source code and test cases, i.e., static code coverage and dynamic code coverage, to schedule test cases. Existing coverage-based TCP techniques mainly focus on maximizing coverage while often do not consider the likely distribution of faults in source code. However, software faults are not often equally distributed in source code, e.g., around 80% faults are located in about 20% source code. Intuitively, test cases that cover the faulty source code should have higher priorities, since they are more likely to find faults. In this paper, we present a quality-aware test case prioritization technique, QTEP, to address the limitation of existing coverage-based TCP algorithms. In QTEP, we leverage code inspection techniques, i.e., a typical statistic defect prediction model and a typical static bug finder, to detect fault-prone source code and then adapt existing coverage-based TCP algorithms by considering the weighted source code in terms of fault-proneness. Our evaluation with 16 variant QTEP techniques on 33 different versions of 7 open source Java projects shows that QTEP could improve existing coverage-based TCP techniques for both regression and new test cases. Specifically, the improvement of the best variant of QTEP for regression test cases could be up to 15.0% and on average 7.6%, and for all test cases (both regression and new test cases), the improvement could be up to 10.0% and on average 5.0%.
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 53 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 10% | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Top 10% | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 10% |
