Error Detection Rate of MC/DC for a Case Study from the Automotive Domain
Chilenski and Miller  claim that the error detection probability of a test set with full modified condition/decision coverage (MC/DC) on the system under test converges to 100% for an increasing number of test cases, but there are also examples where the error detection probability of an MC/DC adequate test set is indeed zero. In this work we analyze the effective error detection rate of a test set that achieves maximum possible MC/DC on the code for a case study from the automotive domain. First we generate the test cases automatically with a model checker. Then we mutate the original program to generate three different error scenarios: the first error scenario focuses on errors in the value domain, the second error scenario focuses on errors in the domain of the variable names and the third error scenario focuses on errors within the operators of the boolean expressions in the decisions of the case study. Applying the test set to these mutated program versions shows that all errors of the values are detected, but the error detection rate for mutated variable names or mutated operators is quite disappointing (for our case study 22% of the mutated variable names, resp. 8% of the mutated operators are not detected by the original MC/DC test set). With this work we show that testing a system with a test set that achieves maximum possible MC/DC on the code detects less errors than expected.