Behavioral models enable the analysis of the functionality of software product lines (SPL), e.g., model checking and model-based testing. Model learning aims to construct behavioral models. Due to the commonalities among the products of an SPL, it is possible to reuse the previously-learned models during the model learning process. In this paper, an adaptive approach, called PL * , for learning the product models of an SPL is presented based on the well-known 𝐿 * algorithm. In this method, after learning each product, the sequences in the final observation table are stored in a repository which is used to initialize the observation table of the remaining products. The proposed algorithm is evaluated on two open-source SPLs and the learning cost is measured in terms of the number of rounds, resets, and input symbols. The results show that for complex SPLs, the total learning cost of PL * is significantly lower than that of the non-adaptive method in terms of all three metrics. Furthermore, it is observed that the order of learning products affects the efficiency of PL * . We introduce a heuristic to determine an ordering which reduces the total cost of adaptive learning. CCS CONCEPTS• Networks → Formal specifications; • Theory of computation → Query learning; • Hardware → Finite state machines; • Software and its engineering → Software product lines.
Behavioral models are the key enablers for behavioral analysis of Software Product Lines (SPL), including testing and model checking. Active model learning comes to the rescue when family behavioral models are non-existent or outdated. A key challenge on active model learning is to detect commonalities and variability efficiently and combine them into concise family models. Benchmarks and their associated metrics will play a key role in shaping the research agenda in this promising field and provide an effective means for comparing and identifying relative strengths and weaknesses in the forthcoming techniques. In this challenge, we seek benchmarks to evaluate the efficiency (e.g., learning time and memory footprint) and effectiveness (e.g., conciseness and accuracy of family models) of active model learning methods in the software product line context. These benchmark sets must contain the structural and behavioral variability models of at least one SPL. Each SPL in a benchmark must contain products that requires more than one round of model learning with respect to the basic active learning 𝐿 * algorithm. Alternatively, tools supporting the synthesis of artificial benchmark models are also welcome.
Real-time computer systems are software or hardware systems which have to perform their tasks according to a time schedule. Formal verification is a widely used technique to make sure if a real-time system has correct time behavior. Using formal methods requires providing support for non-deterministic specification for time constraints which is realized by time intervals. Timed-Rebeca is an actor-based modeling language which is equipped with a verification tool. The values of timing features in this language are positive integer numbers and zero (discrete values). In this paper, Timed-Rebeca is extended to support modeling timed actor systems with time intervals. The semantics of this extension is defined in terms of Interval-Time Transition System (ITTS) which is developed based on the standard semantics of Timed-Rebeca. In ITTS, instead of integer values, time intervals are associated with system states and the notion of shift equivalence relation in ITTS is used to make the transition system finite. As there is a bisimulation relation between the states of ITTS and finite ITTS, it can be used for verification against branching-time properties.
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