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Modern software systems are deployed in a highly dynamic, uncertain environment. Ideally, a system that is robust should be capable of establishing its most critical requirements even in the presence of possible deviations in the environment. We propose a technique called behavioral robustification, which involves systematically and rigorously improving the robustness of a design against potential deviations. Given behavioral models of a system and its environment, along with a set of user-specified deviations, our robustification method produces a redesign that is capable of satisfying a desired property even when the environment exhibits those deviations. In particular, we describe how the robustification problem can be formulated as a multi-objective optimization problem, where the goal is to restrict the deviating environment from causing a violation of a desired property, while maximizing the amount of existing functionality and minimizing the cost of changes to the original design. We demonstrate the effectiveness of our approach on case studies involving the robustness of an electronic voting machine and safety-critical interfaces. 

Software systems are designed and implemented with assumptions about the environment. However, once the system is deployed,the actual environment may deviate from its expected behavior,possibly undermining desired properties of the system. To enable systematic design of systems that are robust against potential environmental deviations, we propose a rigorous notion of robustness for software systems. In particular, the robustness of a system is de-fined as the largest set of deviating environmental behaviors under which the system is capable of guaranteeing a desired property. We describe a new set of design analysis problems based on our notion of robustness, and a technique for automatically computing robustness of a system given its behavior description. We demonstrate potential applications of our robustness notion on two case studies involving network protocols and safety-critical interfaces. 

Plan reuse is a promising approach for enabling self-* systems to effectively adapt to unexpected changes, such as evolving existing adaptation strategies after an unexpected change using stochastic search. An ideal self-* planner should be able to reuse repertoires of adaptation strategies, but this is challenging due to the evaluation overhead. For effective reuse, a repertoire should be both (a) likely to generalize to future situations, and (b) cost effective to evaluate. In this work, we present an approach inspired by chaos engineering for generating a diverse set of adaptation strategies to reuse, and we explore two analysis approaches based on clone detection and syntactic transformation for constructing repertoires of adaptation strategies that are likely to be amenable to reuse in stochastic search self-* planners. An evaluation of the proposed approaches on a simulated system inspired by Amazon Web Services shows planning effectiveness improved by up to 20% and reveals tradeoffs in planning timeliness and optimality.