Showing papers by "Betty H. C. Cheng published in 2013"

Towards run-time testing of dynamic adaptive systems

Abstract: It is challenging to design, develop, and validate a dynamically adaptive system (DAS) that satisfies requirements, particularly when requirements can change at run time. Testing at design time can help verify and validate that a DAS satisfies its specified requirements and constraints. While offline tests may demonstrate that a DAS is capable of satisfying its requirements before deployment, a DAS may encounter unanticipated system and environmental conditions that can prevent it from achieving its objectives. In working towards a requirements-aware DAS, this paper proposes run-time monitoring and adaptation of tests as another technique for evaluating whether a DAS satisfies, or is even capable of satisfying, its requirements given its current execution context. To this end, this paper motivates the need and identifies challenges for adaptively testing a DAS at run time, as well as suggests possible methods for leveraging offline testing techniques for verifying run-time behavior.

Harnessing evolutionary computation to enable dynamically adaptive systems to manage uncertainty

Abstract: This keynote talk and paper intend to motivate research projects that investigate novel ways to model, analyze, and mitigate uncertainty arising in three different aspects of the cyber-physical systems. First, uncertainty about the physical environment can lead to suboptimal, and sometimes catastrophic, results as the system tries to adapt to unanticipated or poorly-understood environmental conditions. Second, uncertainty in the cyber environment can have lead to unexpected and adverse effects, including not only performance impacts (load, traffic, etc.) but also potential threats or overt attacks. Finally, uncertainty can exist with the components themselves and how they interact upon reconfiguration, including unexpected and unwanted feature interactions. Each of these sources of uncertainty can potentially be identified at different stages, respectively run time, design time, and requirements, but their mitigation might be done at the same or a different stage. Based on the related literature and our preliminary investigations, we argue that the following three overarching techniques are essential and warrant further research to provide enabling technologies to address uncertainty at all three stages: model-based development, assurance, and dynamic adaptation. Furthermore, we posit that in order to go beyond incremental improvements to current software engineering techniques, we need to leverage, extend, and integrate techniques from other disciplines.

Exploring automated software composition with genetic programming

Abstract: Much research has been performed in investigating the numerous dimensions of software composition. Challenges include creating a composition-based design process, designing software for reuse, investigating various strategies for composition, and automating the composition process. Depending on the complexity of the relevant components, numerous composition strategies may exist, each of which may have several options and variations for aggregate steps in realizing these strategies. This paper presents an evolutionary computation-based framework for automatically searching for and realizing an optimal composition strategy for composing a given target module into an existing software system.

Validating Code-Level Behavior of Dynamic Adaptive Systems in the Face of Uncertainty

Abstract: A dynamically adaptive system DAS self-reconfigures at run time in order to handle adverse combinations of system and environmental conditions. Techniques are needed to make DASs more resilient to system and environmental uncertainty. Furthermore, automated support to validate that a DAS provides acceptable behavior even through reconfigurations are essential to address assurance concerns. This paper introduces Fenrir, an evolutionary computation-based approach to address these challenges. By explicitly searching for diverse and interesting operational contexts and examining the resulting execution traces generated by a DAS as it reconfigures in response to adverse conditions, Fenrir can discover undesirable behaviors triggered by unexpected environmental conditions at design time, which can be used to revise the system appropriately. We illustrate Fenrir by applying it to a dynamically adaptive remote data mirroring network that must efficiently diffuse data even in the face of adverse network conditions.