Abstract-The software engineering community has proposed numerous approaches for making software self-adaptive. These approaches take inspiration from machine learning and control theory, constructing software that monitors and modifies its own behavior to meet goals. Control theory, in particular, has received considerable attention as it represents a general methodology for creating adaptive systems. Control-theoretical software implementations, however, tend to be ad hoc. While such solutions often work in practice, it is difficult to understand and reason about the desired properties and behavior of the resulting adaptive software and its controller.This paper discusses a control design process for software systems which enables automatic analysis and synthesis of a controller that is guaranteed to have the desired properties and behavior. The paper documents the process and illustrates its use in an example that walks through all necessary steps for self-adaptive controller synthesis.

Modern software applications are subject to uncertain operating conditions, such as dynamics in the availability of services and variations of system goals. Consequently, runtime changes cannot be ignored, but often cannot be predicted at design time. Control theory has been identified as a principled way of addressing runtime changes and it has been applied successfully to modify the structure and behavior of software applications. Most of the times, however, the adaptation targeted the resources that the software has available for execution (CPU, storage, etc.) more than the software application itself. This paper investigates the research efforts that have been conducted to make software adaptable by modifying the software rather than the resource allocated to its execution. This paper aims to identify: the focus of research on control-theoretical software adaptation; how software is modeled and what control mechanisms are used to adapt software; what software qualities and controller guarantees are considered. To that end, we performed a systematic literature review in which we extracted data from 42 primary studies selected from 1512 papers that resulted from an automatic search. The results of our investigation show that even though the behavior of software is considered non-linear, research efforts use linear models to represent it, with some success. Also, the control strategies that are most often considered are classic control, mostly in the form of Proportional and Integral controllers, and Model Predictive Control. The paper also discusses sensing and actuating strategies that are prominent for software adaptation and the (often neglected) proof of formal properties. Finally, we distill open challenges for control-theoretical software adaptation.

An increasingly important concern of software engineers is handling uncertainties at design time, such as environment dynamics that may be difficult to predict or requirements that may change during operation. The idea of self-adaptation is to handle such uncertainties at runtime, when the knowledge becomes available. As more systems with strict requirements require self-adaptation, providing guarantees for adaptation has become a high-priority. Providing such guarantees with traditional architecture-based approaches has shown to be challenging. In response, researchers have studied the application of control theory to realize self-adaptation. However, existing control-theoretic approaches applied to adapt software systems have primarily focused on satisfying only a single adaptation goal at a time, which is often too restrictive for real applications. In this paper, we present Simplex Control Adaptation, SimCA, a new approach to self-adaptation that satisfies multiple goals, while being optimal with respect to an additional goal. SimCA offers robustness to measurement inaccuracy and environmental disturbances, and provides guarantees. We evaluate SimCA for two systems with strict requirements that have to deal with uncertainties: an underwater vehicle system used for oceanic surveillance, and a tele-assistance system for health care support. CCS Concepts• Software and its engineering → Designing software; • Computing methodologies → Computational control theory;

The pervasiveness and growing complexity of software systems is challenging software engineering to design systems that can adapt their behavior to withstand unpredictable, uncertain, and continuously changing execution environments. Control theoretical adaptation mechanisms received a growing interest from the software engineering community in the last years for their mathematical grounding allowing formal guarantees on the behavior of the controlled systems. However, most of these mechanisms are tailored to specific applications and can hardly be generalized into broadly applicable software design and development processes.This paper discusses a reference control design process, from goal identification to the verification and validation of the controlled system. A taxonomy of the main control strategies is introduced, analyzing their applicability to software adaptation for both functional and non-functional goals. A brief extract on how to deal with uncertainty complements the discussion. Finally, the paper highlights a set of open challenges, both for the software engineering and the control theory research communities.

Today customers require software systems to provide particular levels of qualities, while operating under dynamically changing conditions. These requirements can be met with di↵erent self-adaptation approaches. Recently, we developed two approaches that are di↵erent in nature -control theorybased SimCA and architecture-based ActivFORMS -to endow software systems with self-adaptation, providing guarantees on desired behavior. However, it is unclear which of the two approaches should be used in di↵erent adaptation scenarios and how e↵ective they are in comparison to each other. In this paper, we apply SimCA and ActivFORMS to the Tele Assistance System (TAS) exemplar and compare obtained results, demonstrating the di↵erence in achieved qualities and formal guarantees.

Recent advances in embedded systems and underwater communications raised the autonomy levels in unmanned underwater vehicles (UUVs) from human-driven and scripted to adaptive and self-managing. UUVs can execute longer and more challenging missions, and include functionality that enables adaptation to unexpected oceanic or vehicle changes. As such, the simulated UUV exemplar UNDERSEA introduced in our paper facilitates the development, evaluation and comparison of self-adaptation solutions in a new and important application domain. UNDERSEA comes with predefined oceanic surveillance UUV missions, adaptation scenarios, and a reference controller implementation, all of which can easily be extended or replaced.

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Self-adaptation provides a principled way to deal with software systems’ uncertainty during operation. Examples of such uncertainties are disturbances in the environment, variations in sensor readings, and changes in user requirements. As more systems with strict goals require self-adaptation, the need for formal guarantees in self-adaptive systems is becoming a high-priority concern. Designing self-adaptive software using principles from control theory has been identified as one of the approaches to provide guarantees. In general, self-adaptation covers a wide range of approaches to maintain system requirements under uncertainty, ranging from dynamic adaptation of system parameters to runtime architectural reconfiguration. Existing control-theoretic approaches have mainly focused on handling requirements in the form of setpoint values or as quantities to be optimized. Furthermore, existing research primarily focuses on handling uncertainty in the execution environment. This article presents SimCA*, which provides two contributions to the state-of-the-art in control-theoretic adaptation: (i) it supports requirements that keep a value above and below a required threshold, in addition to setpoint and optimization requirements; and (ii) it deals with uncertainty in system parameters, component interactions, system requirements, in addition to uncertainty in the environment. SimCA* provides guarantees for the three types of requirements of the system that is subject to different types of uncertainties. We evaluate SimCA* for two systems with strict requirements from different domains: an Unmanned Underwater Vehicle system used for oceanic surveillance and an Internet of Things application for monitoring a geographical area. The test results confirm that SimCA* can satisfy the three types of requirements in the presence of different types of uncertainty.