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Description:
Computer systems today are no longer monolithic programs; instead they usually comprise multiple interacting programs. With the continuous growth of these systems and with their integration into systems of systems, interoperability becomes a fundamental issue. Integration of systems is more complex and occurs more frequently than ever before. One solution to this problem could be the automated model-based synthesis of mediators at runtime. However, this approach has strong prerequisites. It requires the existence of adequate models of the systems to be connected. Many systems encountered in practice, on the other hand, do not come with models. In such cases models have to be constructed ex post (at runtime). Furthermore, adequate models must capture control as well as data aspects of a system. In most protocols, for instance, data parameters (e.g., session identifiers or sequence numbers) can influence system behavior. Models of such systems can be thought of as interface programs: Rather than covering only the control behavior, they describe explicitly which data values are relevant to the communication and have to be remembered and reused. This thesis addresses the problem of inferring interface programs of systems at runtime using active automata learning techniques. Active automata learning uses a test-based and counterexample-driven approach to inferring models of black-box systems. The method has originally been introduced for finite automata (the popular L* algorithm). Extending active learning to interface programs requires research in three directions: First, the efficiency of active learning algorithms has to be optimized to scale when dealing with data parameters. Second, techniques are needed for finding counterexamples driving the learning process in practice. Third, active learning has to be extended to richer models than Mealy machines or DFAs, capable of expressing interface programs. The work presented in this thesis improves the state of the art in all three directions. More concretely, the ...