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We propose a novel methodology for validating software product line (PL)models by integrating Statistical Model Checking (SMC) with Process Mining(PM). Our approach focuses on the feature-oriented language QFLan in the PLengineering domain, allowing modeling of PLs with rich cross-tree andquantitative constraints, as well as aspects of dynamic PLs like stagedconfigurations. This richness leads to models with infinite state-space,requiring simulation-based analysis techniques like SMC. For instance, weillustrate with a running example involving infinite state space. SMC involvesgenerating samples of system dynamics to estimate properties such as eventprobabilities or expected values. On the other hand, PM uses data-driventechniques on execution logs to identify and reason about the underlyingexecution process. In this paper, we propose, for the first time, applying PMtechniques to SMC simulations' byproducts to enhance the utility of SMCanalyses. Typically, when SMC results are unexpected, modelers must determinewhether they stem from actual system characteristics or model bugs in ablack-box manner. We improve on this by using PM to provide a white-boxperspective on the observed system dynamics. Samples from SMC are fed into PMtools, producing a compact graphical representation of observed dynamics. Themined PM model is then transformed into a QFLan model, accessible to PLengineers. Using two well-known PL models, we demonstrate the effectiveness andscalability of our methodology in pinpointing issues and suggesting fixes.Additionally, we show its generality by applying it to the security domain.

White-box validation of quantitative product lines by statistical model checking and process mining