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Models such as finite state automata are widely used to abstract the behaviorof software systems by capturing the sequences of events observable duringtheir execution. Nevertheless, models rarely exist in practice and, when theydo, get easily outdated; moreover, manually building and maintaining models iscostly and error-prone. As a result, a variety of model inference methods thatautomatically construct models from execution traces have been proposed toaddress these issues.  However, performing a systematic and reliable accuracy assessment of inferredmodels remains an open problem. Even when a reference model is given, mostexisting model accuracy assessment methods may return misleading and biasedresults. This is mainly due to their reliance on statistical estimators over afinite number of randomly generated traces, introducing avoidable uncertaintyabout the estimation and being sensitive to the parameters of the random tracegenerative process.  This paper addresses this problem by developing a systematic approach basedon analytic combinatorics that minimizes bias and uncertainty in model accuracyassessment by replacing statistical estimation with deterministic accuracymeasures. We experimentally demonstrate the consistency and applicability ofour approach by assessing the accuracy of models inferred by state-of-the-artinference tools against reference models from established specification miningbenchmarks.

Rigorous Assessment of Model Inference Accuracy using Language Cardinality