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While massive strides have been made in the field of search-based software testing (SBST) in recent years, there yet remains the problem of transitioning such techniques to reality. This paper discusses this problem in terms of cyber-physical systems, presents research challenges for applying SBST to this domain, and outlines the state-of-the-art achievements of the SBST community in this regard. 

The number of patients diagnosed with Alzheimer's disease is significantly increasing, given the boom in the aging population (i.e., 65 years and older). There exist approximately 5.5 million people in the United States that have been diagnosed with Alzheimer's, and as a result friends and family often need to provide care and support (estimated at 15 million people to the cost of $1.1 trillion). Common symptoms of Alzheimer's disease include memory loss, drastic behavioral change, depression, and loss in cognitive and/or spatial abilities. To support the growing need for caregivers, this project developed a prototype virtual reality (VR) environment for enabling caregivers to experience typical scenarios, as well as common strategies for managing each scenario, that they may experience when providing care and support, thereby providing. For instance, a patient may turn on a gas stove and then leave, forgetting that the stove is on. The caregiver then would be required to turn the stove off, to minimize any potential dangers. The prototype environment, CARETAKVR, was developed as an undergraduate research project for learning the process of research as well as the Unity programming environment and VR. The prototype provides a gamified training tool, masking scenarios as objectives and success with a score, to enable the potential caregiver to feel rewarded for correctly supporting the patient. The virtual patient is controlled via artificial intelligence and follows an initial set of guidelines to behave as a patient with early-stage Alzheimer's may behave. The caregiver is provided with a set of tasks to perform, in VR space, to achieve their goals for each scenario. Common tasks include Check Refrigerator, Check Stove, and Comfort Patient. This project has been demonstrated to colleagues in the health care domain and has seeded future collaborations to iterate the capabilities of this tool. All project artifacts have been open-sourced and are available online. 

The increased growth of the aging population (i.e., 65 years or older) has led to emerging technologies in health care that provide in-home support to patients using devices throughout the household. Such smart home environments can monitor and interact with patients and their doctors/caregivers to augment patient medical data for diagnosis than can be generated via traditional doctor visits. Moreover, smart homes are enabling older adults to stay at home longer as opposed to permanent moves to assisted living or nursing facilities, increasing health and well-being and decreasing overall costs to the individual and society at large. This paper proposes Cognitive Assisted Living (CAL), a cyber-physical system comprising a network of embedded devices for collecting and analyzing patient speech patterns over time for monitoring cognitive function beginning in the early stages of Alzheimer’s disease. Specifically, CAL will analyze patient speech patterns and spatial abilities, via a set of daily interactions, to provide a longitudinal analysis of speech deterioration, a significant indicator of cognitive decline resulting from Alzheimer’s disease. Understanding the rate of cognitive decline can enable caregivers and health care professionals to better manage the patient’s daily care and medical requirements. Additionally, the patient’s cognitive state can be shared across household devices to increase the patient’s comfort and better accommodate lifestyle changes. To these ends, we describe the architecture of the proposed system, the methods to which we will detect cognitive decline, and specify how the system will provide continuing fault tolerance and data security at run time. 

A self-adaptive system (SAS) can reconfigure at run time in response to uncertainty and/or adversity to continually deliver an acceptable level of service. An SAS can experience uncertainty during execution in terms of environmental conditions for which it was not explicitly designed as well as unanticipated combinations of system parameters that result from a self-reconfiguration or misunderstood requirements. Run-time testing provides assurance that an SAS continually behaves as it was designed even as the system reconfigures and the environment changes. Moreover, introducing adaptive capabilities via lightweight evolutionary algorithms into a run-time testing framework can enable an SAS to effectively update its test cases in response to uncertainty alongside the SAS's adaptation engine while still maintaining assurance that requirements are being satisfied. However, the impact of the evolutionary parameters that configure the search process for run-time testing may have a significant impact on test results. Therefore, this paper provides an empirical study that focuses on the mutation parameter that guides online evolution as applied to a run-time testing framework, in the context of an SAS. 

A self-adaptive system (SAS) can reconfigure at run time in response to adverse combinations of system and environmental conditions in order to continuously satisfy its requirements. Moreover, SASs are subject to cross-cutting non-functional requirements (NFRs), such as performance, security, and usability, that collectively characterize how functional requirements (FRs) are to be satisfied. In many cases, the trigger for adapting an SAS may be due to a violation of one or more NFRs. For a given NFR, different combinations of hierarchically-organized FRs may yield varying degrees of satisfaction (i.e., satisficement). This paper presents Providentia, a search-based technique to optimize NFR satisficement when subjected to various sources of uncertainty (e.g., environment, interactions between system elements, etc.). Providentia searches for optimal combinations of FRs that, when considered with different subgoal decompositions and/or differential weights, provide optimal satisficement of NFR objectives. Experimental results suggest that using an SAS goal model enhanced with search-based optimization significantly improves system performance when compared with manually and randomly-generated weights and subgoals.