基于偶然正确性概率的回归测试选择方法

数据驱动的智能系统的核心是处理数据的算法,对算法正确性的要求高,导致其测试开销大,需要有效地缩减测试的规模,其中回归测试选择是控制测试规模的有效手段.数据驱动的智能系统由于其动态信息流强度弱的原因,发生偶然正确性现象的概率较高,并且该现象会导致常用的回归测试选择技术所选择出的测试集包含大量检测不到故障的测试用例.因此,...

Regression Test Selection Method Based on Coincidental Correctness Probability

For data-driven intelligent systems, the data processing algorithms are very important and need to be tested adequately. Because of the high safety requirement, the cost of testing becomes very high and need to be reduced. Regression test selection is an effective mean to control the scale of testing. For data-driven intelligent systems, the coincidental correctness happens frequently because of the weak dynamic information flows, and leads that the regression test sets contain a lot of redundant tests. Therefore, we propose a regression test selection technique based on the coincidental correctness probability. This method considers the probability of coincidental correctness in addition to the code coverage. The selected tests not only cover the modified code, but have a higher probability to transfer the intermediate results produced by the modified code to the program output. Such selection can reduce the impact of coincidental correctness. The empirical results show that our selection method can improve the precision of selection and reduce the size of the regression tests.