Automatic B-model repair using model checking and machine learning

摘要

The B-method, which provides automated verification for the design of software systems, still requires users to manually repair faulty models. This paper proposes B-repair, an approach that supports automated repair of faulty models written in the B formal specification language. After discovering a fault in a model using the B-method, B-repair is able to suggest possible repairs for the fault, estimate the quality of suggested repairs and use a suitable repair to revise the model. The suggestion of repairs is produced using the Isolation method, which suggests changing the pre-conditions of operations, and the Revision method, which suggests changing the post-conditions of operations. The estimation of repair quality makes use of machine learning techniques that can learn the features of state transitions. After estimating the quality of suggested repairs, the repairs are ranked, and a best repair is selected according to the result of ranking and is used to revise the model. This approach has been evaluated using a set of finite state machines seeded with faults and a case study. The evaluation has revealed that B-repair is able to repair a large number of faults, including invariant violations, assertion violations and deadlock states, and gain high accuracies of repair. Using the combination of model checking and machine learning-guided techniques, B-repair saves development time by finding and repairing faults automatically during design.