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.

An increasingly used method for the engineering of software systems with strict quality-of-service (QoS) requirements involves the synthesis and verification of probabilistic models for many alternative architectures and instantiations of system parameters. Using manual trial-and-error or simple heuristics for this task often produces suboptimal models, while the exhaustive synthesis of all possible models is typically intractable. The EvoChecker search-based software engineering approach presented in our paper addresses these limitations by employing evolutionary algorithms to automate the model synthesis process and to significantly improve its outcome. EvoChecker can be used to synthesise the Pareto-optimal set of probabilistic models associated with the QoS requirements of a system under design, and to support the selection of a suitable system architecture and configuration. EvoChecker can also be used at runtime, to drive the efficient reconfiguration of a self-adaptive software system. We evaluate EvoChecker on several variants of three systems from different application domains, and show its effectiveness and applicability. Keywords Search-based software engineering • Probabilistic model checking • Evolutionary algorithms • QoS requirements B Simos Gerasimou

Self-adaptive systems used in safety-critical and businesscritical applications must continue to comply with strict non-functional requirements while evolving in order to adapt to changing workloads, environments, and goals. Runtime quantitative verification (RQV) has been proposed as an effective means of enhancing self-adaptive systems with this capability. However, RQV frequently fails to provide the fast response times and low computation overheads required by real-world self-adaptive systems. In this paper, we investigate how three techniques, namely caching, lookahead and nearly-optimal reconfiguration, and combinations thereof, can help address this limitation. Extensive experiments in a case study involving the RQV-driven self-adaptation of an unmanned underwater vehicle indicate that these techniques can lead to significant reductions in RQV response times and computation overheads.

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Deep Learning (DL) systems are key enablers for engineering intelligent applications due to their ability to solve complex tasks such as image recognition and machine translation. Nevertheless, using DL systems in safety-and security-critical applications requires to provide testing evidence for their dependable operation. Recent research in this direction focuses on adapting testing criteria from traditional software engineering as a means of increasing confidence for their correct behaviour. However, they are inadequate in capturing the intrinsic properties exhibited by these systems. We bridge this gap by introducing DeepImportance, a systematic testing methodology accompanied by an Importance-Driven (IDC) test adequacy criterion for DL systems. Applying IDC enables to establish a layer-wise functional understanding of the importance of DL system components and use this information to guide the generation of semantically-diverse test sets. Our empirical evaluation on several DL systems, across multiple DL datasets and with state-of-the-art adversarial generation techniques demonstrates the usefulness and effectiveness of DeepImportance and its ability to guide the engineering of more robust DL systems.

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We present DECIDE, a rigorous approach to decentralising the control loops of distributed self-adaptive software used in missioncritical applications. DECIDE uses quantitative verification at runtime, first to agree individual component contributions to meeting systemlevel quality-of-service requirements, and then to ensure that components achieve their agreed contributions in the presence of changes and failures. All verification operations are carried out locally, using component-level models, and communication between components is infrequent. We illustrate the application of DECIDE and show its effectiveness using a case study from the unmanned underwater vehicle domain.

Abstract-We present a method for the synthesis of software system designs that satisfy strict quality requirements, are Paretooptimal with respect to a set of quality optimisation criteria, and are robust to variations in the system parameters. To this end, we model the design space of the system under development as a parametric continuous-time Markov chain (pCTMC) with discrete and continuous parameters that correspond to alternative system architectures and to the ranges of possible values for configuration parameters, respectively. Given this pCTMC and required tolerance levels for the configuration parameters, our method produces a sensitivity-aware Pareto-optimal set of designs, which allows the modeller to inspect the ranges of quality attributes induced by these tolerances, thus enabling the effective selection of robust designs. Through application to two systems from different domains, we demonstrate the ability of our method to synthesise robust designs with a wide spectrum of useful tradeoffs between quality attributes and sensitivity.Keywords-software performance and reliability engineering; probabilistic model synthesis; multi-objective optimisation I. INTRODUCTION Evaluating the performance, reliability and other quality attributes of alternative designs is essential for the cost-effective engineering of software [1], [2]. Delaying this evaluation until integration or system testing can greatly increase engineering costs, as defects identified late in the development lifecycle require much more effort to fix [3]. A common method to avoid this delay uses model-based simulation [4] or formal verification [5] to predict the quality attributes of alternative designs. Models that meet the quality requirements of the system under development are then used as a basis for its implementation. Models based on queueing networks [6], probabilistic models [2], [5] and timed automata [7] have been used for this purpose, together with tools for their simulation (e.g. Palladio [8]) and verification (e.g. PRISM [9]). Furthermore, recently proposed approaches automate the search for suitable designs. Probabilistic model repair [10], [11] automatically modifies the transition probabilities of Markov models that violate a quality requirement, generating new models that meet the requirement. Precise parameter synthesis [12] identifies transition rates that enable continuous Markov models to satisfy a quality requirement or to optimise a quality attribute of the modelled system. Finally, probabilistic model synthesis [13] starts from a design template that captures alternative system designs, and uses multiobjective optimisation to generate the Pareto-optimal set of Markov models associated with the quality requirements of the system.