GK-Tail+ An Efficient Approach to Learn Software Models

Inferring models of program behavior from execution samples can provide useful information about a system, also in the increasingly common case of systems that evolve and adapt in their lifetime, and without requiring large developers' effort. Techniques for learning models of program behavior from execution traces shall address conflicting challenges of recall, specificity and performance: They shall generate models that comprehensively represent the system behavior (recall) while limiting the amount of illegal behaviors that may be erroneously accepted by the model (specificity), and should infer the models within a reasonable time budget to process industrial scale systems (performance). In our early work, we designed GK-Tail, an approach that can infer guarded finite state machines that model the behavior of object-oriented programs in terms of sequences of method calls and constraints on the parameter values. GK-Tail addresses well two of the three main challenges, since it infers guarded finite state machines with a high level of recall and specificity, but presents severe limitations in terms of performance that reduce its scalability. In this paper, we present GK-Tail+, a new approach to infer guarded finite state machines from execution traces of object-oriented programs. GK-Tail+ proposes a new set of inference criteria that represent the core element of the inference process: It largely reduces the inference time of GK-Tail while producing guarded finite state machines with a comparable level of recall and specificity. Thus, GK-Tail+ advances the preliminary results of GK-Tail by addressing all the three main challenges of learning models of program behavior from execution traces.