A genetic algorithm approach for test case optimization of safety critical control

Safety plays a key role in the safe operation of any safety critical control systems. Safety in such systems depends on the correct operation of the software meant for the safety purpose. Thorough testing of software is required to avoid the catastrophic accidents or to minimize the failure. As a case study, benchmark problem is tested against the C code according to the required specification of the system. Due to complexity involved in the control system there is a need to create a set of test inputs automatically. This paper describes the generation of optimized test cases to ensure block coverage metrics using Genetic Algorithm and results are compared with the Taguchi design of experiments. Random error seeding is carried out into the code to study the efficacy of the test cases.