Abstract-Building on concepts drawn from control theory, self-adaptive software handles environmental and internal uncertainties by dynamically adjusting its architecture and parameters in response to events such as workload changes and component failures. Self-adaptive software is increasingly expected to meet strict functional and non-functional requirements in applications from areas as diverse as manufacturing, healthcare and finance. To address this need, we introduce a methodology for the systematic ENgineering of TRUstworthy Self-adaptive sofTware (ENTRUST). ENTRUST uses a combination of (1) design-time and runtime modelling and verification, and (2) industry-adopted assurance processes to develop trustworthy self-adaptive software and assurance cases arguing the suitability of the software for its intended application. To evaluate the effectiveness of our methodology, we present a tool-supported instance of ENTRUST and its use to develop proof-of-concept self-adaptive software for embedded and service-based systems from the oceanic monitoring and e-finance domains, respectively. The experimental results show that ENTRUST can be used to engineer self-adaptive software systems in different application domains and to generate dynamic assurance cases for these systems.

One major challenge in self-adaptive systems is to assure the required quality properties. Formal methods provide the means to rigorously specify and reason about the behaviors of self-adaptive systems, both at design time and runtime. To the best of our knowledge, no systematic study has been performed on the use of formal methods in self-adaptive systems. As a result, there is no clear view on what methods have been used to verify self-adaptive systems, and what support these methods offer to software developers. As such insight is important for researchers and engineers, we performed a systematic literature review covering 12 main software engineering venues and 4 journals, resulting in 75 papers used for data collection. The study shows that the attention for selfadaptive software systems is gradually increasing, but the number of studies that employ formal methods remains low. The main focus of formalization is on modeling and reasoning. Model checking and theorem proving have gained limited attention. The main concerns of interest in formalization of self-adaptation are efficiency/performance and reliability. Important adaptation concerns, such as security and scalability, are hardly considered. To verify the concerns of interest, a set of new properties are defined, such as interference freedom, responsiveness, mismatch, and loss-tolerance. A relevant part of the studies use formal methods at runtime, but the use is limited to modeling and analysis. Formal methods can be applied to other runtime activities of self-adaptation, and there is a need for lightweight tools to support runtime verification.

Self-adaptation enables a software system to deal autonomously with uncertainties, such as dynamic operating conditions that are difficult to predict or changing goals. A common approach to realize self-adaptation is with a MAPE-K feedback loop that consists of four adaptation components: Monitor, Analyze, Plan, and Execute. These components share Knowledge models of the managed system, its goals and environment. To provide guarantees of the adaptation goals, state of the art approaches propose using formal models of the knowledge. However, less attention is given to the formalization of the adaptation components themselves, which is important to provide guarantees of correctness of the adaptation behavior (e.g., does the execute component execute the plan correctly?). We propose Active FORmal Models for Self-adaptation (ActivFORMS) that uses an integrated formal model of the adaptation components and knowledge models. The formal model is directly executed by a virtual machine to realize adaptation, hence active model. The contributions of ActivFORMS are: (1) the approach assures that the adaptation goals that are verified offline are guaranteed at runtime, and (2) it supports dynamic adaptation of the active model to support changing goals. We show how we have applied ActivFORMS for a small-scale robotic system.

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