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.

No abstract

No abstract

Self-adaptation is a promising approach to manage the complexity of modern software systems. A self-adaptive system is able to adapt autonomously to internal dynamics and changing conditions in the environment to achieve particular quality goals. Our particular interest is in decentralized selfadaptive systems, in which central control of adaptation is not an option. One important challenge in self-adaptive systems, in particular those with decentralized control of adaptation, is to provide guarantees about the intended runtime qualities. In this paper, we present a case study in which we use model checking to verify behavioral properties of a decentralized self-adaptive system. Concretely, we contribute with a formalized architecture model of a decentralized traffic monitoring system and prove a number of self-adaptation properties for flexibility and robustness. To model the main processes in the system we use timed automata, and for the specification of the required properties we use timed computation tree logic. We use the Uppaal tool to specify the system and verify the flexibility and robustness properties.

Abstract-Providing high-quality software in the face of uncertainties, such as dealing with new user needs, changing availability of resources, and faults that are difficult to predict, raises fundamental challenges to software engineers. These challenges have motivated the need for self-adaptive systems. One of the primary claimed benefits of self-adaptation is that a design with external feedback loops provide a more effective engineering solution for self-adaptation compared to a design with internal mechanisms. While many efforts indicate the validity of this claim, to the best of our knowledge, no controlled experiments have been performed that provide scientifically founded evidence for it. Such experiments are crucial for researchers and engineers to underpin their claims and improve research. In this paper, we report the results of a controlled experiment performed with 24 final-year students of a Master in Software Engineering program in which designs based on external feedback loops are compared with designs based on internal mechanisms. The results show that applying external feedback loops can reduce control flow complexity and fault density, and improve productivity. We found no evidence for a reduction of activity complexity.

No abstract