3 research outputs found
Automatic Steering of Behavioral Model Inference
Get PDFMany testing and analysis techniques use finite state mod-els to validate and verify the quality of software systems. Since the specification of such models is complex and time-consuming, researchers defined several techniques to extract finite state models from code and traces. Automatically generating models requires much less effort than designing them, and thus eases the verification and validation of large software systems. However, when models are inferred au-tomatically, the precision of the mining process is critical. Behavioral models mined with imprecise processes can in-clude many spurious behaviors, and can thus compromise the results of testing and analysis techniques that use those models. In this paper, we increase the precision of automata in-ferred from execution traces, by leveraging two learning tech-niques. We first mine execution traces to infer statistically significant temporal properties that capture relations be-tween non consecutive and possibly distant events. We then incrementally refine a simple initial automaton by merg-ing likely equivalent states. We identify equivalent states by analyzing set of consecutive events, and we use the in-ferred temporal properties to evaluate whether two equiv-alent states can be merged or not. We merge equivalent states only if the merging does violate any temporal prop-erty, since a merging that violates temporal properties is likely to introduce an imprecise generalization. Our gener-alization process that preserves temporal properties while merging states avoids breaking non-local relations, and thus solves one of the major cause of overgeneralized models. Thus, mined properties steer the learning of behavioral mod-els. The technique is completely automated and generates an automaton that both accepts the input traces and satis-fies the mined temporal properties. ∗This work has been partially supported by the Europea
