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We present preliminary insights into the perception of researchers and practitioners of the software product line (SPL) community on previous, current, and future research efforts. We were particularly interested in up-and-coming and outdated topics and whether the views of academics and industry researchers differ. Also, we compared the views of the community with the results of an earlier literature survey published at SPLC 2018. We conducted a questionnaire-based survey with attendees of SPLC 2018. We received 33 responses (about a third of the attendees) from both, very experienced attendees and younger researchers, and from academics as well as industry researchers. We report preliminary findings regarding popular and unpopular SPL topics, topics requiring further work, and industry versus academic researchers' views. Differences between academic and industry researchers become visible only when analyzing comments on open questions. Most importantly, while topics popular among respondents are also popular in the literature, topics respondents think require further work have often already been well researched. We conclude that the SPL community needs to do a better job preserving and communicating existing knowledge and particularly also needs to widen its scope.

Empirical studies indicate that user experience can significantly be improved in model-driven engineering. Blended modelling aims at mitigating this by enabling users to interact with a single model through different notations. Blended modelling contributes to various modelling qualities, including comprehensibility, analysability, and acceptability. In this paper, we define the notion of blended modelling and propose a set of dimensions that characterise blended modelling. The dimensions are grouped in two classes: user-oriented dimensions and realisation-oriented dimensions. Each dimension describes a facet that is relevant to blended modelling together with its domain (i.e., the range of values for that dimension). The dimensions offer a basic vocabulary to support tool developers with making well-informed design decisions as well as users to select appropriate tools and configure them according to the needs at hand. We illustrate how the dimensions apply to different cases relying on our experience with blended modelling. We discuss the impact of blended modelling on usability and user experience and sketch metrics to measure it. Finally, we outline a number of core research directions in this increasingly important modelling area.

Student engagement, i.e. the intensity and emotional quality of students’ involvement in initiating and carrying out learning activities, is an important predictor for academic performance. However, at KU Leuven, we observe that student engagement in bachelor program courses computer science is often low, which may lead to superficial knowledge of the learning material. Interactions with colleagues at other universities indicate similar issues. The question is then, how can we increase student engagement? Research has shown that the educational context can affect engagement and a good fit between the students and the context is key in transforming student’ abilities into achievement. An important approach to increase the fit between students and the educational context is flipped classroom. Flipped classroom is a pedagogical approach that originates from active learning introduced by A. King in 1993. In a flipped classroom the activities that traditionally take place inside the classroom now take place outside the classroom and vice versa. Benefits have been found for flipped classroom such as enhanced engagement, effective education through student-student/teacher interactions, and students becoming more responsible learners. Recently, flipped classroom had gained increasing attention in higher education, also in computer science and software engineering in particular. To investigate and tackle the issue of poor student engagement in our Bachelor program, we introduced a flipped classroom approach in a basic software engineering course. Instead of letting students taking online courses (videos) before the lectures and then applying the obtained knowledge during lectures, we designed a different flipped classroom approach centered on enhancing students’ responsibility in the learning process. In doing so, we aimed at tackling the specific challenges with poor engagement of students in bachelor courses. Responsibly-centered learning follows an iterative process, where in each cycle a well-defined part of the course material is handled. A cycle starts with the docent defining specific goals for the part and preparing focus material. The students use this focus material to study the relevant course material with a course handbook, online material, and interactions with fellow students. The students then prepare and submit topics for discussion. The docent groups the topics in discussion themes and announces them. Students express their preferences for moderating the themes during a deepening session. The themes are allocated to students based on their preferences. Then follows the deepening session where students elaborate on the discussion themes moderated by one of the students. The docent provides instruction of specific problems on request. After the session, students can submit a summary of the topics for the part. Throughout the course, feedback sessions are organized, and at the end of the course an exam is organized that focuses on the course goals. The summaries submitted per part can be used during the exam. We have applied the approach in a software engineering course in two academic years. The experience shows that responsibly-centered learning approach supports students becoming owners of the learning process, and students report that the approach fosters their engagement and enables them to obtain deeper understanding of the course material. We reflect on lessons learned and outline plans for the future.

As any software system, a self-adaptive system is subject to security threats. However, applying self-adaptation may introduce additional threats. So far, little research has been devoted to this important problem. In this paper, we propose an approach for vulnerability analysis of architecture-based adaptations in self-adaptive systems using threat modeling and analysis techniques. To this end, we specify components' vulnerabilities and the system architecture formally and generate an attack model that describes the attacker's strategies to attack the system by exploiting different types of vulnerabilities. We use a set of security metrics to quantitatively assess the security risks of adaptations based on the produced attack model which enables the system to consider security aspects while choosing an adaptation to apply to the system. We automate and incorporate our approach into the Rainbow framework, allowing secure architectural adaptations at runtime. To evaluate the effectiveness of our approach, we apply it on a simple document storage system and on the ZNN system.

Architecting self-adaptive software systems is challenging. These systems must achieve their goals not only in the environment in which they are deployed initially, but also as this environment changes over time. When self-adaptive systems are used in safety-critical and business-critical applications, this challenge is compounded by the need to also provide guarantees that the system operates correctly at all times. For traditional software systems, such guarantees are provided through assurance cases. These are structured arguments which use comprehensive development-time evidence to explain why a system can be trusted when used for its planned application in a given environment. This tutorial presents the ENTRUST methodology for achieving a similar level of trust in self-adaptive systems [1]. ENTRUST is the first end-to-end methodology for architecting trustworthy self-adaptive systems and dynamic assurance cases guaranteeing the suitability of the software for its intended applications. As advocated by major research initiatives such as the UK-led Assuring Autonomy International Programme (https://www.york.ac.uk/assuring-autonomy/). ENTRUST dynamic assurance cases seamlessly combine evidence obtained during the development of a self-adaptive software system with evidence obtained from its additional verification at runtime. As such, each reconfiguration of an ENTRUST self-adaptive system is accompanied by a new version of the assurance case that confirms the trustworthiness of the reconfigured system architecture.

Software engineering is an intrinsically collaborative activity, especially in the era of Agile Software Development. Many actors are partaking in development activities, such that a common understanding should be reached at numerous stages during the overall development life-cycle. For a few years now, Natural Language Processing techniques have been employed either to extract key information from free-form text or to generate models from the analysis of text in order to ease the sharing of knowledge across all parties. A significant part of these approaches focuses on retrieving lost domain and architectural knowledge through the analysis of documents, issue management systems or other forms of knowledge management systems. However, these post-processing methods are time-consuming by nature since they require to invest significant resources into the validation of the extracted knowledge. In this paper, inspired by collaborative tools, bots and Natural Language extraction approaches, we envision new ways to collaboratively record and document design decisions as they are discussed. These decisions will be documented as they are taken and, for some of them, static or behavioural models may be generated on-the-fly. Such an interactive process will ensure everyone agrees on critical design aspects of the software. We believe development teams will benefit from this approach because manual encoding of design knowledge will be reduced and will not be pushed to a later stage, when not forgotten.

Modern software systems are expected to operate under uncertain conditions, without interruption. Possible causes of uncertainties include changes in the operational environment, dynamics in the availability of resources, and variations of user goals. The aim of self-adaptation is to let the system collect additional data about the uncertainties during operation. The system uses the additional data to resolve uncertainties, to reason about itself, and based on its goals to reconfigure or adjust itself to satisfy the changing conditions, or if necessary to degrade gracefully. In this chapter, we provide a particular perspective on the evolution of the field of self-adaptation in six waves. These waves put complementary aspects of engineering self-adaptive systems in focus that synergistically have contributed to the current knowledge in the field. From the presented perspective on the field, we outline a number of challenges for future research in self-adaptation, both in a short and long term.

International audience; From smart buildings to medical devices to smart nations, software systems increasingly integrate computation, networking, and interaction with the physical environment. These systems are known as Cyber-Physical Systems (CPS). While these systems open new opportunities to deliver improved quality of life for people and reinvigorate computing, their engineering is a difficult problem given the level of heterogeneity and dynamism they exhibit. While progress has been made, we argue that complexity is now at a level such that existing approaches need a major rethink to define principles and associated techniques for CPS. In this paper, we identify research challenges when modelling, analysing and engineering CPS. We focus on three key topics: theoretical foundations of CPS, self-adaptation methods for CPS, and exemplars of CPS serving as a research vehicle shared by a larger community. For each topic, we present an overview and suggest future research directions, thereby focusing on selected challenges. This paper is one of the results of the Shonan Seminar 118 on Modelling and Analysing Resilient Cyber-Physical Systems, which took place in December 2018.

Collective Intelligence Systems (CIS), such as wikis and social networks, enable enhanced knowledge creation and sharing at organization and society levels. From our experience in R&D projects with industry partners and in-house CIS development, we learned that these platforms go through a complex evolution process. A particularly challenging aspect in this respect represents uncertainties that can appear at any time in the life-cycle of such systems. A prominent way to deal with uncertainties is adaptation, i.e., the ability to adjust or reconfigure the system in order to mitigate the impact of the uncertainties. However, there is currently a lack of consolidated design knowledge of CIS-specific adaptation and methods for managing it. To support software architects, we contribute an architecture viewpoint for continuous adaptation management in CIS, aligned with ISO/IEC/IEEE 42010. We evaluated the viewpoint in a case study with a group of eight experienced engineers. The results show that the viewpoint is well-structured, useful and applicable, and that its model kinds cover well the scope to handle different CIS-specific adaptation problems.

Control theory has contributed a set of foundational techniques to handle “change” at runtime in software applications. These techniques however pose a number of challenges as well: (i) they require the development and understanding of mathematical models; (ii) synthesizing solutions is often done on a per-problem basis, discouraging flexibility and generality. Software engineering, as a discipline, has always aimed at finding reusable and modular solutions. The combination of the desire to apply formally grounded control-theoretical principles and reuse existing solutions has motivated research on the topic of automatically generated control solutions. This research aims at designing control strategies in an automated way from data that qualifies the given problem at hand. This chapter provides an overview of the research topic of automatically generated control-theoretical solutions, explaining the key research contributions and paving the way for future research. ispartof: Engineering Adaptive Software Systems pages:35-55 ispartof: pages:35-55 status: accepted

Self-adaptation is an established approach to deal with uncertainties that are difficult to predict before a system is deployed. A self-adaptative system employs a feedback loop that tracks changes and adapts the system accordingly to ensure its quality goals. However, making effective adaptation decisions at runtime is challenging. In this chapter we tackle two problems of effective decision making in self-adaptive systems. First, current research typically focusses on the benefits adaptaton can bring but ignores the cost of adaptation, which may invalidate the expected benefits. To tackle this problem, we introduce CB@R (Cost-Benefit analysis @ Runtime), a novel model-based approach for runtime decision-making in self-adaptive systems that handles both the benefits and costs of adaptation as first-class citizens in decision making. Second, we look into the adaptation space of self-adaptive systems, i.e. the set of adaption options to select from. For systems with a large number of adaptation options, analyzing the entire adaptation space is often not feasible given the time and resources constraints at hand. To tackle this problem, we present a machine learning approach that integrates learning with the feedback loop to select a subset of the adaption options that are valid in the current situation. We evaluate CB@R and the learning approach for a real world deployed Internet of Things (IoT) application.

When a self-adaptive system detects that its adaptation goals may be compromised, it needs to determine how to adapt to ensure its goals. To that end, the system can analyze the possible options for adaptation, i.e., the adaptation space, and pick the best option that achieves the goals. Such analysis can be resource and time consuming, in particular when rigorous analysis methods are applied. Hence, exhaustively analyzing all options may be infeasible for systems with large adaptation spaces. This problem is further complicated as the adaptation options typically include uncertainty parameters that can only be resolved at runtime. In this paper, we present a machine learning approach to tackle this problem. This approach enhances the traditional MAPE-K feedback loop with a learning module that selects subsets of adaptation options from a large adaptation space to support the analyzer with performing efficient analysis. We instantiate the approach for two concrete learning techniques, classification and regression, and evaluate the approaches for two instances of an Internet of Things application for smart environment monitoring with different sizes of adaptation spaces. The evaluation shows that both learning approaches reduce the adaptation space significantly without noticeable effect on realizing the adaptation goals. ispartof: pages:1-12 ispartof: Proceedings of the 14th International Symposium on Software Engineering for Adaptive and Self-Managing Systems pages:1-12 ispartof: International Symposium on Software Engineering for Adaptive and Self-Managing Systems location:Montreal, QC, Canada, Canada date:25 May - 26 May 2019 status: Published online

Modern software-intensive systems are increasingly blending cyber, physical, and social elements, demanding higher degrees of autonomy and adaptability than ever before. In combination with the ever growing integration and scale of systems, and the inherent uncertainties modern systems face, the principles from MAS engineering remain particularly attractive for engineering systems in a wide variety of domains today. In this chapter, we reflect on the role and potential of MAS engineering on a selection of key facets that characterize modern software engineering practice. We focus at facets that we believe are important in relation to MAS engineering. Concretely, we look at agile development, Cloud and edge computing, distributed ledgers and blockchain, Cyber-Physical Systems and Internet-of-Things, and finally green computing. For each of these facets we highlight opportunities to EMAS engineering, but also the challenges these facets raise. We conclude with highlighting a number of ethical issues that the engineers of modern software-intensive systems and thus also MAS will face in the years to come.

Many of today's software systems accommodate different usage and deployment scenarios. Intentional and unintentional variability in functionality or quality attributes (e.g., performance) of software significantly increases the complexity of the problem and design space of those systems. The complexity caused by variability becomes increasingly difficult to handle due to the increasing size of software systems, new and emerging application domains, dynamic operating conditions under which software systems have to operate, fast moving and highly competitive markets, and more powerful and versatile hardware. This paper reports results of the first International Workshop on Variability and Complexity in Software Design that brought together researchers and engineers interested in the topic of complexity and variability. It also outlines directions the field might move in the future

Methodological Guidelines for Modeling and Developing MAS-Based Simulations The intersection of agents, modeling, simulation, and application domains has been the subject of active research for over two decades. Although agents and simulation have been used effectively in a variety of application domains, much of the supporting research remains scattered in the literature, too often leaving scientists to develop multi-agent system (MAS) models and simulations from scratch. Multi-Agent Systems: Simulation and Applications provides an overdue review of the wide ranging facets of MAS simulation, including methodological and application-oriented guidelines. This comprehensive resource reviews two decades of research in the intersection of MAS, simulation, and different application domains. It provides scientists and developers with disciplined engineering approaches to modeling and developing MAS-based simulations. After providing an overview of the fields history and its basic principles, as well as cataloging the various simulation engines for MAS, the book devotes three sections to current and emerging approaches and applications. Simulation for MAS explains simulation support for agent decision making, the use of simulation for the design of self-organizing systems, the role of software architecture in simulating MAS, and the use of simulation for studying learning and stigmergic interaction. MAS for Simulation discusses an agent-based framework for symbiotic simulation, the use of country databases and expert systems for agent-based modeling of social systems, crowd-behavior modeling, agent-based modeling and simulation of adult stem cells, and agents for traffic simulation. Tools presents a number of representative platforms and tools for MAS and simulation, including Jason, James II, SeSAm, and RoboCup Rescue. Complete with over 200 figures and formulas, this reference book provides the necessary overview of experiences with MAS simulation and the tools needed to exploit simulation in MAS for future research in a vast array of applications including home security, computational systems biology, and traffic management.

The study presented in this paper aims to provide evidence for the hypothesis that software product line research has been changing and that the works in industry and academia have diverged over time. We analysed a subset (140) of all (593) papers published at the Software Product Line Conference (SPLC) until 2017. The subset was randomly selected to cover all years as well as types of papers. We assessed the research type of the papers (academic or industry), the kind of evaluation (application example, empirical, etc.), and the application domain. Also, we assessed which product line life-cycle phases, development practices, and topics the papers address. We present an analysis of the topics covered by academic vs. industry research and discuss the evolution of these topics and their relation over the years. We also discuss implications for researchers and practitioners. We conclude that even though several topics have received more attention than others, academic and industry research on software product lines are actually rather in line with each other.

© 2018 IEEE. Engineering software-intensive systems, such as production systems, is complex as these systems are subject to various types of changes that are often difficult to anticipate before deployment. Tackling this complexity requires joint expertise from different backgrounds. In this paper we focus on the problem of maintaining online technical documentation of telecom systems. In the context of continuous deployment and ever-changing user needs, high quality of the documentation of such products is in a key concern of users. To tackle this problem, different experts worked together equipping the online documentation system with a feedback loop. This feedback loop tracks changes in the system and its context and automatically adapts the documentation accordingly. The results demonstrate that this self-adaptation approach offers a viable solution to tackle the maintainability problem of online documentation of telecom systems. ispartof: pages:1375-1380 ispartof: Proceedings of 14th IEEE International Conference on Automation Science and Engineering (CASE) vol:2018-August pages:1375-1380 ispartof: 14th IEEE International Conference on Automation Science and Engineering (CASE) location:Munich, Germany date:20 Aug - 24 Aug 2018 status: published

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. ispartof: IEEE Transactions on Software Engineering vol:44 issue:8 pages:784-810 status: published

Empirical software engineering (SE) research is often criticized for poorly designed and reported studies, a lack of replications to build up bodies of knowledge, and little practical relevance. In this paper, we discuss issues in empirical software architecture research as an illustration of these issues in one subfield of SE and as a step towards better understanding empirical research in SE in general. Based on feedback from software architecture researchers and practitioners, we explore why, despite persistent discussions in the SE research community, there are still disagreements about why and how to conduct empirical research. Then, we explore how empirical SE research can progress beyond "one-off" studies and endless "new and exciting" results toward SE research as a mature science. This would allow us to establish foundations for evaluating existing and future empirical research and help researchers design and publish better studies.

Software systems are increasingly expected to cope with variable workloads, component failures and other uncertainties through self-adaptation. As such, self-adaptive software has been the subject of intense research over the past decade [3, 4, 9, 10].

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., 29 pages, 24 figures

Internet of Things (IoT) are in full expansion. Applications range from factory floors to smart city environments. IoT applications consist of battery powered small computing devices (motes) that communicate wirelessly and interact with the environment through sensors and actuators. A key challenge that IoT engineers face is how to manage such systems that are subject to inherent uncertainties in their operation contexts, such as interferences and dynamic traffic in the network. Often these uncertainties are difficult to predict at development time. In practice, IoT applications are therefore typically over-provisioned at deployment; however, this leads to inefficiency. In this paper, we make a case for IoT applications that manage themselves at runtime to deal with uncertainties. We contribute: (1) a set of concerns that motivate the need for self-management for IoT systems, (2) three initial approaches that illustrate the potential of realising self-managing IoT systems, and (3) a set of open challenges for future research on self-adaptation in IoT. ispartof: pages:67-84 ispartof: SOFSEM 2018: THEORY AND PRACTICE OF COMPUTER SCIENCE vol:10706 pages:67-84 ispartof: International Conference on Current Trends in Theory and Practice of Computer Science location:Vienna date:29 Jan - 2 Feb 2018 status: published

Architecture-based adaptation equips a software-intensive system with a feedback loop that enables the system to adapt itself at runtime to changes to maintain its required quality goals. To guarantee the required goals, existing adaptation approaches apply exhaustive verification techniques at runtime. However these approaches are restricted to small-scale settings, which often limits their applicability in practice. To tackle this problem, we introduce an innovative architecture-based adaptation approach to solve a concrete practical problem of VersaSense: automating the management of Internet-of-Things (IoT). The approach, called MARTAS, equips a software system with a feedback loop that employs Models At Run Time and Statistical techniques to reason about the system and adapt it to ensure the required goals. We apply MARTAS to a building security case system, which is a representative IoT system deployed by VersaSense. The application comprises a set of IoT devices that communicate sensor data over a time synchronized smart mess network to a central monitoring facility. We demonstrate how MARTAS outperforms a conservative approach that is typically applied in practice and a state-of-the-art adaptation approach for different quality goals, and we report lessons learned from this industrial case.

Designing software systems that have to deal with dynamic operating conditions, such as changing availability of resources and faults that are difficult to predict, is complex. A promising approach to handle such dynamics is self-adaptation that can be realized by a MAPE-K feedback loop (Monitor-Analyze-Plan-Execute plus Knowledge). To provide evidence that the system goals are satisfied, given the changing conditions, the state of the art advocates the use of formal methods. However, little research has been done on consolidating design knowledge of self-adaptive systems. To support designers, this paper contributes with a set of formally specified MAPE-K templates that encode design expertise for a family of self-adaptive systems. The templates comprise: (1) behavior specification templates for modeling the different components of a MAPE-K feedback loop (based on networks of timed automata), and (2) property specification templates that support verification of the correctness of the adaptation behaviors (based on timed computation tree logic). To demonstrate the reusability of the formal templates, we performed four case studies in which final-year Masters students used the templates to design different self-adaptive systems.

Context Handling multiple quality attributes (QAs) in the domain of self-adaptive systems is an understudied research area. One well-known approach to engineer adaptive software systems and fulfill QAs of the system is architecture-based self-adaptation. In order to develop models that capture the required knowledge of the QAs of interest, and to investigate how these models can be employed at runtime to handle multiple quality attributes, we need to first examine current architecture-based self-adaptive methods. Objective In this paper we review the state-of-the-art of architecture-based methods for handling multiple QAs in self-adaptive systems. We also provide a descriptive analysis of the collected data from the literature. Method We conducted a systematic literature review by performing an automatic search on 28 selected venues and books in the domain of self-adaptive systems. As a result, we selected 54 primary studies which we used for data extraction and analysis. Results Performance and cost are the most frequently addressed set of QAs. Current self-adaptive systems dealing with multiple QAs mostly belong to the domain of robotics and web-based systems paradigm. The most widely used mechanisms/models to measure and quantify QAs sets are QA data variables. After QA data variables, utility functions and Markov chain models are the most common models which are also used for decision making process and selection of the best solution in presence of many alternatives. The most widely used tools to deal with multiple QAs are PRISM and IBM's autonomic computing toolkit. KLAPER is the only language that has been specifically developed to deal with quality properties analysis. Conclusions Our results help researchers to understand the current state of research regarding architecture-based methods for handling multiple QAs in self-adaptive systems, and to identity areas for improvement in the future. To summarize, further research is required to improve existing methods performing tradeoff analysis and preemption, and in particular, new methods may be proposed to make use of models to handle multiple QAs and to enhance and facilitate the tradeoffs analysis and decision making mechanism at runtime. ispartof: Information and Software Technology vol:90 pages:1-26 status: published

© 2017 IEEE. 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. ispartof: pages:83-89 ispartof: Proceedings of the International Symposium of Software Engineering for Adaptive and Self-Managing Systems pages:83-89 ispartof: International Symposium of Software Engineering for Adaptive and Self-Managing Systems status: accepted

The important concern for modern software systems is to become more cost-effective, while being versatile, flexible, resilient, dependable, energy-efficient, customisable, configurable and self-optimising when reacting to run-time changes that may occur within the system itself, its environment or requirements. One of the most promising approaches to achieving such properties is to equip software systems with self-managing capabilities using self-adaptation mechanisms. Despite recent advances in this area, one key aspect of self-adaptive systems that remains to be tackled in depth is the provision of assurances, i.e., the collection, analysis and synthesis of evidence that the system satisfies its stated functional and non-functional requirements during its operation in the presence of self-adaptation. The provision of assurances for self-adaptive systems is challenging since run-time changes introduce a high degree of uncertainty. This paper on research challenges complements previous roadmap papers on software engineering for self-adaptive systems covering a different set of topics, which are related to assurances, namely, perpetual assurances, composition and decomposition of assurances, and assurances obtained from control theory. This research challenges paper is one of the many results of the Dagstuhl Seminar 13511 on Software Engineering for Self-Adaptive Systems: Assurances which took place in December 2013.

Context The underlying uncertainty in self-adaptive systems aggravates the complexity of selecting best adaptation action alternative, and handling requirements trade-offs. To efficiently tackle uncertainty, it is necessary to have a comprehensive overview of different types of uncertainty and their specifications. Objective In this paper we aim at (a) reviewing the state-of-the-art of architecture-based approaches tackling uncertainty in self-adaptive systems with multiple quality requirements, (b) proposing a classification framework for this domain, and (c) classifying the current approaches according to this framework. Method We conducted a systematic literature review by performing an automatic search on twenty seven selected venues and books in the domain of self-adaptive systems. Results We propose a classification framework for uncertainty and its sources in the domain of architecture-based self-adaptive systems with multiple quality requirements. We map 51 identified primary studies into the framework and present the classified results. Conclusions Our results help researchers to understand the current state of research regarding uncertainty in architecture-based self-adaptive systems with multiple concerns, and identity areas for improvement in the future.

© 2017 IEEE. Physical telerehabilitation services over the Internet allow physiotherapists to engage in remote consultation with patients at their homes, improving the quality of care and reducing costs. Traditional visual approaches, such as webcams and videophones, are limited in terms of precision of assessment and support for assistance with exercises. In this paper, we present a Physical Telerehabilitation System (PTS) that enhances video interaction with IoT technology to monitor the position of the body of patients in space and provide smart data to physiotherapists and users. We give an overview of the architecture of the PTS and evaluate (i) its usability based on a number of interviews and focus groups with stakeholders, and (ii) its technical efficiency based on a series of measurements. From this evaluation, we derive a number of challenges for further improvement of the PTS and outline a possible solution based on a microservices architecture. ispartof: pages:112-119 ispartof: Proceedings of the International Workshop on Engineering IoT Systems: Architectures, Services, Applications, and Platforms pages:112-119 ispartof: IEEE International Conference on Software Architecture (ICSA) location:Göteborg, SWEDEN date:3 Apr - 7 Apr 2017 status: published

Context: Variability (i.e., the ability of software systems or artifacts to be adjusted for different contexts) became a key property of many systems. Objective: We analyze existing research on variability in software systems. We investigate variability handling in major software engineering phases (e.g., requirements engineering, architecting). Method: We performed a systematic literature review. A manual search covered 13 premium software engineering journals and 18 premium conferences, resulting in 15,430 papers searched and 196 papers considered for analysis. To improve reliability and to increase reproducibility, we complemented the manual search with a targeted automated search. Results: Software quality attributes have not received much attention in the context of variability. Variability is studied in all software engineering phases, but testing is underrepresented. Data to motivate the applicability of current approaches are often insufficient; research designs are vaguely described. Conclusions: Based on our findings we propose dimensions of variability in software engineering. This empirically grounded classification provides a step towards a unifying, integrated perspective of variability in software systems, spanning across disparate or loosely coupled research themes in the software engineering community. Finally, we provide recommendations to bridge the gap between research and practice and point to opportunities for future research.

Smart Cyber-Physical Systems (sCPS) are a novel kind of Cyber- Physical Systems engineered to take advantage of large-scale cooperation between devices, users and environment to achieve added value in face of uncertainty and various situations in their environment. Examples of sCPS include modern traffic systems, Industry 4.0 systems, systems for smart-buildings, smart energy grids, etc. The uniting aspect of all these systems is that to achieve their high-level of intelligence, adaptivity and ability to optimize and learn, they heavily rely on software. This makes them software-intensive systems, where software becomes their most complex part. Engineering sCPS thus becomes a recognized software engineering discipline, which however, due to specifics of sCPS, can only partially rely on the existing body of knowledge in software engineering. In fact, it turns out that many of the traditional approaches to architecture modeling and software development fail to cope with the high dynamicity and uncertainty of sCPS. This calls for innovative approaches that jointly reflect and address the specifics of such systems. This paper maps the discussions and results of the Third International Workshop on Software Engineering for Smart Cyber-Physical Systems (SEsCPS 2017), which specifically focuses on challenges and promising solutions in the area of software engineering for sCPS.

Context: Empirical research helps gain well-founded insights about phenomena. Furthermore, empirical research creates evidence for the validity of research results. Objective: We aim at assessing the state-of-practice of empirical research in software architecture. Method: We conducted a comprehensive survey based on the systematic mapping method. We included all full technical research papers published at major software architecture conferences between 1999 and 2015. Results: 17% of papers report empirical work. The number of empirical studies in software architecture has started to increase in 2005. Looking at the number of papers, empirical studies are about equally frequently used to a) evaluate newly proposed approaches and b) to explore and describe phenomena to better understand software architecture practice. Case studies and experiments are the most frequently used empirical methods. Almost half of empirical studies involve human participants. The majority of these studies involve professionals rather than students. Conclusions: Our findings are meant to stimulate researchers in the community to think about their expectations and standards of empirical research. Our results indicate that software architecture has become a more mature domain with regards to applying empirical research. However, we also found issues in research practices that could be improved (e.g., when describing study objectives and acknowledging limitations).

Designing a software architecture requires architectural reasoning, i.e., activities that translate requirements to an architecture solution. Architectural reasoning is particularly challenging in the design of product-lines of self-adaptive systems, which involve variability both at development time and runtime. In previous work we developed an extended Architectural Reasoning Framework (eARF) to address this challenge. However, evaluation of the eARF showed that the framework lacked support for rigorous reasoning, ensuring that the design complies to the requirements. In this paper, we introduce an analytical framework that enhances eARF with such support. The framework defines a set of artifacts and a series of activities. Artifacts include templates to specify domain quality attribute scenarios, concrete models, and properties. The activities support architects with transforming requirement scenarios to architecture models that comply to required properties. Our focus in this paper is on architectural reasoning support for a single product instance. We illustrate the benefits of the approach by applying it to an example client-server system, and outline challenges for future work.

Context: Cyber-physical systems (CPS) seamlessly integrate computational and physical components. Adaptability, realized through feedback loops, is a key requirement to deal with uncertain operating conditions in CPS. Objective: We aim at assessing state-of-art approaches to handle self-adaptation in CPS at the architectural level. Method: We conducted a systematic literature review by searching four major scientific data bases, resulting in 1103 candidate studies and eventually retaining 42 primary studies included for data collection after applying inclusion and exclusion criteria. Results: The primary concerns of adaptation in CPS are performance, flexibility, and reliability. 64% of the studies apply adap- tation at the application layer and 24% at the middleware layer. MAPE (Monitor-Analyze-Plan-Execute) is the dominant adaptation mechanism (60%), followed by agents and self-organization (both 29%). Remarkably, 36% of the studies combine differ- ent mechanisms to realize adaptation; 17% combine MAPE with agents. The dominating application domain is energy (24%). Conclusions: Our findings show that adaptation in CPS is a cross-layer concern, where solutions combine different adaptation mechanisms within and across layers. This raises challenges for future research both in the field of CPS and self-adaptation, including: how to map concerns to layers and adaptation mechanisms, how to coordinate adaptation mechanisms within and across layers, and how to ensure system-wide consistency of adaptation.

© Springer International Publishing AG 2016. In the model-centric approach to model-driven development, the models used are sufficiently detailed to be executed. Being able to execute the model directly, without any intermediate model-to-code translation, has a number of advantages. The model is always up-to-date and runtime updates of the model are possible. This paper presents a model interpreter for timed automata, a formalism often used for modeling and verification of real-time systems. The model interpreter supports real-time system features like simultaneous execution, system wide signals, a ticking clock, and time constraints. Many existing formal representations can be verified, and many existing DSMLs can be executed. It is the combination of being both verifiable and executable that makes our approach rather unique. ispartof: pages:243-258 ispartof: LEVERAGING APPLICATIONS OF FORMAL METHODS, VERIFICATION AND VALIDATION: FOUNDATIONAL TECHNIQUES, PT I vol:9952 pages:243-258 ispartof: Leveraging Applications of Formal Methods, Verification and Validation: Foundational Techniques location:Corfu, Greece date:10 Oct - 14 Oct 2016 status: published

© 2016 IEEE. Modern software systems are subject to uncertainties, such as dynamics in the availability of resources or changes of system goals. Self-adaptation enables a system to reason about runtime models to adapt itself and realises its goals under uncertainties. Our focus is on providing guarantees for adaption goals. A prominent approach to provide such guarantees is automated verification of a stochastic model that encodes up-to-date knowledge of the system and relevant qualities. The verification results allow selecting an adaption option that satisfies the goals. There are two issues with this state of the art approach: i) changing goals at runtime (a challenging type of uncertainty) is difficult, and ii) exhaustive verification suffers from the state space explosion problem. In this paper, we propose a novel modular approach for decision making in self-adaptive systems that combines distinct models for each relevant quality with runtime simulation of the models. Distinct models support on the fly changes of goals. Simulation enables efficient decision making to select an adaptation option that satisfies the system goals. The tradeoff is that simulation results can only provide guarantees with a certain level of accuracy. We demonstrate the benefits and tradeoffs of the approach for a service-based telecare system. ispartof: pages:1-9 ispartof: Proceeding Models at Runtime, Würzburg 2016 pages:1-9 ispartof: Models at Runtime location:Würzburg date:18 Jul - 18 Jul 2016 status: published