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Behavioral models enable the analysis of the functionality of softwareproduct lines (SPL), e.g., model checking and model-based testing. Modellearning aims at constructing behavioral models for software systems in someform of a finite state machine. Due to the commonalities among the products ofan SPL, it is possible to reuse the previously learned models during the modellearning process. In this paper, an adaptive approach (the $\text{PL}^*$method) for learning the product models of an SPL is presented based on thewell-known $L^*$ algorithm. In this method, after model learning of eachproduct, the sequences in the final observation table are stored in arepository which will be used to initialize the observation table of theremaining products to be learned. The proposed algorithm is evaluated on twoopen-source SPLs and the total learning cost is measured in terms of the numberof rounds, the total number of resets and input symbols. The results show thatfor complex SPLs, the total learning cost for the $\text{PL}^*$ method issignificantly lower than that of the non-adaptive learning method in terms ofall three metrics. Furthermore, it is observed that the order in which theproducts are learned affects the efficiency of the $\text{PL}^*$ method. Basedon this observation, we introduced a heuristic to determine an ordering whichreduces the total cost of adaptive learning in both case studies.

Adaptive Behavioral Model Learning for Software Product Lines