Generating Realistic Logically Unreasonable Faulty Data for Fault Injection

摘要

In fault injection, we can use a logical constraint as an interface description and negate the constraint to derive logically unreasonable faulty data in order to test the dependability of a system. However, the existing constraint-based approaches only use constraint solving to generate brand new data for testing. Because the given constraints are often incomplete, such brand new data may not satisfy all the hidden constraints and hence can be nonrealistic. Besides, there can be many different strategies to negate a constraint in order to derive constraint-unsatisfied faulty data. Which negation strategy is the best choice for high coverage fault injection is still unclear. To these ends, this paper presents a new constraint-based fault injection technique which relaxes the constraint variables instead of solving brand new data for fault injection. With such an approach, the generated data can be more close to the original non-faulty data and hence are likely to be more realistic. We also investigated the effectiveness of different negation strategies on a constraint formula for fault injection. The experimental results indicate that our constraint relaxing approach does produce faulty data closer to the original ones. The results also provide insights for the application of constraint negation strategies in fault injection.