For the de-identification of health datasets, OLA is an improvement on existing k-anonymity algorithms in terms of information loss and performance.

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Regardless of which (model-centric or code-centric) development process is adopted, industrial software production ultimately and necessarily requires the delivery of an executable implementation. It is generally accepted that the quality of such an implementation is of utmost importance. Yet current verification techniques, including software testing, remain problematic. In this paper, we focus on acceptance testing, that is, on the validation of the actual behavior of the implementation under test against the requirements of stakeholder(s). This task must be as objective and automated as possible. Our first goal is to review existing code-based and model-based tools for testing in light of what such an objective and automated approach to acceptance testing entails. Our contention is that the difficulties we identify originate mainly in a lack of traceability between a testable model of the requirements of the stakeholder(s) and the test cases used to validate these requirements. We then investigate whether such traceability is addressed in other relevant specification-based approaches.

One of the most crucial and complicated phases of realtime system development lies in the transition from system behavior (generally specified using scenario models) to the behavior of interacting components (typically captured by means of communicating hierarchical finite state machines). It is commonly accepted that a systematic approach is required for this transition. In this paper, we overview such an approach, which we root in a hierarchy of "behavior integration patterns" we have elaborated. The proposed patterns guide the structuring of a component's behavior, and help in integrating the behavior associated with new scenarios into the existing hierarchical finite state machine of a component. One of these patterns is discussed at length here.