Complete IOCO test cases: a case study

Abstract

Input/Output Transition Systems (IOTSs) have been widely used as test models in model-based testing. Traditionally, input output conformance testing (IOCO) has been used to generate random test cases from IOTSs. A recent test case generation method for IOTSs, called Complete IOCO, applies fault models to obtain complete test suites with guaranteed fault coverage for IOTSs. This paper measures the efficiency of Complete IOCO in comparison with the traditional IOCO test case generation implemented in the JTorX tool. To this end, we use a case study involving five specification models from the automotive and the railway domains. Faulty mutations of the specifications were produced in order to compare the efficiency of both test generation methods in killing them. The results indicate that Complete IOCO is more efficient in detecting deep faults in large state spaces while IOCO is more efficient in detecting shallow faults in small state spaces.

Efficient state synchronisation in model-based testing through reinforcement learning

“…In Figure 1 1 , we provided the specification for the ceiling speed monitoring with service brake intervention (SBI) from [63]. Note that this specifications is partial.…”