A Comparison of Fault Detection Efficiency Between Adaptive Random Testing and Greedy Combinatorial Testing for Control Logics in Nuclear Industrial Distributed Control Systems

Due to the complexity of the nuclear industrial distributed control system (DCS), input-domain testing techniques, including random testing and combinatorial testing, are usually utilized to test the control logics in nuclear industrial DCS. To improve the fault detection efficiency of random testing, the adaptive random testing technique selects a test case that significantly differs from all existing test cases. Similarly, to improve the fault detection efficiency of combinatorial testing, the greedy combinatorial testing technique adopts a greedy strategy to generate test cases that cover more uncovered tuple-combinations of parametric values. In this paper, we designed an experiment to compare the fault detection efficiency between adaptive random testing technique and greedy combinatorial testing technique for control logics of nuclear industrial DCS. Through the analysis of the fault detection ratios, the $f$ -measure values, and the values of average percent of faults detected (APFD) on two experimental subjects, including the commonly used benchmarks in the field of Boolean-specification testing as well as a group of Boolean expressions extracted from the control logics in nuclear industrial DCS, the experimental results give us the following conclusions: (1) If the test suites’ sizes are relatively small, the fault detection efficiencies of the two techniques are very close though there is a slight advantage in adaptive random testing; (2) With the gradual increase of test suites’ sizes, the fault detection efficiency of greedy combinatorial testing is beyond adaptive random testing gradually. Such a result can help us select the appropriate testing techniques in the testing of the control logics in nuclear industry DCS.