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Requirements-driven approaches provide an effective mechanism for self-adaptive systems by reasoning over their runtime requirements models to make adaptation decisions. However, such approaches usually assume that the relations among alternative behaviours, environmental parameters and requirements are clearly understood, which is often simply not true. Moreover, they do not consider the influence of the current behaviour of an executing system on adaptation decisions. In this paper, we propose an improved requirementsdriven self-adaptation approach that combines goal reasoning and case-based reasoning. In the approach, past experiences of successful adaptations are retained as adaptation cases, which are described by not only requirements violations and contexts, but also currently deployed behaviours. The approach does not depend on a set of original adaptation cases, but employs goal reasoning to provide adaptation solutions when no similar cases are available. And case-based reasoning is used to provide more precise adaptation decisions that better reflect the complex relations among requirements violations, contexts, and current behaviours by utilizing past experiences. Our experimental study with an online shopping benchmark shows that our approach outperforms both requirements-driven approach and case-based reasoning approach in terms of adaptation effectiveness and overall quality of the system.

In Online-to-Offline (O2O) commerce, customer services may need to be composed from online and offline services. Such composition is challenging, as it requires effective selection of appropriate services that, in turn, support optimal combination of both online and offline services. In this paper, we address this challenge by proposing an approach to O2O service composition which combines offline route planning and social collaboration to optimize service selection. We frame general O2O service composition problems using timed automata and propose an optimization procedure that incorporates: (1) a Markov Chain Monte Carlo (MCMC) algorithm to stochastically select a concrete composite service, and (2) a model checking approach to searching for an optimal collaboration plan with the lowest cost given certain time constraint. Our procedure has been evaluated using the simulation of a rich scenario on effectiveness and scalability.