Towards Generating Realistic and High Coverage Test Data for Constraint-Based Fault Injection

Generating faulty data is a key issue in fault injection. The faulty data include not only the ones of extreme values or bad formats, but also the ones which are logically unreasonable. Constraint-based fault injection which negates interface constraints to solve faulty data is effective for logically unreasonable data generation. However, the existing constraint-based approaches just solve brand new data for testing. Such brand new data may easily violate some hidden environment constraints on the test inputs and hence be nonrealistic. Besides, there can be different strategies to negate a constraint in order to derive the constraint-unsatisfied faulty data. What are the possible negation strategies and which strategies are better for high coverage fault injection are still unclear. To these ends, this paper presents a new constraint-based fault injection approach. The approach introduces 10 different strategies for constraint negation and relaxes constraint variables to generate faulty data instead of solving brand new data for fault injection. It can produce faulty data which are closer to the original non-faulty ones and hence likely to be more realistic. We experimentally investigated the effectiveness and cost of the introduced constraint negation strategies. The results provide insights for the application of these strategies in fault injection.