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SummaryTesting of concurrent software written in programming languages like Java and C/C++ is a highly challenging task owing to the many possible interactions among threads. A simple, cheap, and effective approach that addresses this challenge is testing with noise injection, which influences the scheduling so that different interleavings of concurrent actions are witnessed. In this paper, multiple results achieved recently in the area of noise‐injection‐based testing by the authors are presented in a unified and extended way. In particular, various concurrency coverage metrics are presented first. Then, multiple heuristics for solving the noise placement problem (i.e. where and when to generate noise) as well as the noise seeding problem (i.e. how to generate the noise) are introduced and experimentally evaluated. In addition, several new heuristics are proposed and included into the evaluation too. Recommendations on how to set up noise‐based testing for particular scenarios are then given. Finally, a novel use of the genetic algorithm for finding suitable combinations of the many parameters of tests and noise techniques is presented. Copyright © 2014 John Wiley & Sons, Ltd.

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MIDAS (Model Integrated Decomposition and Assisted Specification) is an innovative new approach to requirement specification. By integrating textual and intent‐based model‐assisted requirement flows, MIDAS presents a paradigm shift from prior model‐based and traditional textual requirement processes. Such a change is necessary to address the full intent of design assurance. The goal of MIDAS is to address the many challenges we have observed in applying the current model‐based certification guidance. The principal issue with interpreting the current guidance is a misplaced focus on the separation of graphical vs. textual specification rather than a clean and clear separation of design from intent. Hence, this aspect forms the core of our proposed MIDAS approach. Our intent‐based focus also mitigates the often implementation‐centric perspective of model‐based requirement flows. Our hybrid model‐based constrained language flow also addresses the weakness of informal natural language‐based specifications. Finally, MIDAS facilitates the integration of knowledge management with requirement engineering, allowing easier visualization of the full requirement context. By writing this paper, we share our observations and ideas openly to encourage feedback and collaboration with potential development partners.