Software quality is critical in today's software systems. A challenge is the trade-off situation architects face in the design process. Designers often have two or more alternatives, which must be compared and put into context before a decision is made. The challenge becomes even more complex for dynamic software product lines, where domain designers have to take runtime variations into consideration as well. To address the problem we propose extensions to an architectural reasoning framework with constructs/artifacts to define and model a domain's scope and dynamic variability. The extended reasoning framework encapsulates knowledge to understand and reason about domain quality behavior and self-adaptation as a primary variability mechanism. The framework is demonstrated for a self-configuration property, self-upgradability on an educational product-line.

Software architecture serves as a foundation for the design and development of software systems. Designing an architecture requires extensive analysis and reasoning. The study presented herein focuses on the architectural analysis and reasoning in support of engineering self-adaptive software systems with systematic reuse. Designing self-adaptive software systems with systematic reuse introduces variability along three dimensions; adding more complexity to the architectural analysis and reasoning process. To this end, the study presents an extended Architectural Reasoning Framework with dedicated reasoning support for self-adaptive systems and reuse. To evaluate the proposed framework, we conducted an initial feasibility case study, which concludes that the proposed framework assists the domain architects to increase reusability, reduce fault density, and eliminate differences in skills and experiences among architects, which were our research goals and are decisive factors for a system's overall quality.

Advances in computing technologies are pushing software systems and their operating environments to become more dynamic and complex. The growing complexity of software systems coupled with uncertainties induced by runtime variations leads to challenges in software analysis and design. Self-Adaptive Software Systems (SASS) have been proposed as a solution to address design time complexity and uncertainty by adapting software systems at runtime. A vast body of knowledge on engineering self-adaptive software systems has been established. However, to the best of our knowledge, no or little work has considered systematic reuse of this knowledge. To that end, this study contributes an Autonomic Software Product Lines engineering (ASPLe) methodology. The ASPLe is based on a multi-product lines strategy which leverages systematic reuse through separation of application and adaptation logic. It provides developers with repeatable process support to design and develop self-adaptive software systems with reuse across several application domains. The methodology is composed of three core processes, and each process is organized for requirements, design, implementation, and testing activities. To exemplify and demonstrate the use of the ASPLe methodology, three application domains are used as running examples throughout the report.

This work studies systematic reuse in the context of self-adaptive software systems. In our work, we realized that managing variability for such platforms is different compared to traditional platforms, primarily due to the run-time variability and system uncertainties. Motivated by the fact that recent trends show that self-adaptation will be used more often in future system generation and that software reuse state-of-practice or research do not provide sufficient support, we have investigated the problems and possibly resolutions in this context. We have analyzed variability for these systems, using a systematic reuse prism, and identified a research gap in variability management. The analysis divides variability handling into four activities: (1) identify variability, (2) constrain variability, (3) implement variability, and (4) manage variability. Based on the findings we envision a reuse framework for the specific domain and present an example framework that addresses some of the identified challenges. We argue that it provides basic support for engineering self-adaptive software systems with systematic reuse. We discuss some important avenues of research for achieving the vision.

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. © 2016 IEEE.

We describe ongoing work on a variability mechanism for Autonomic Software Product Lines (ASPL). The autonomic software product lines have self-management characteristics that make product line instances more resilient to context changes and some aspects of product line evolution. Instances sense the context, selects and bind the best component variants to variation-points at run-time. The variability mechanism we describe is composed of a profile guided dispatch based on off-line and on-line training processes. Together they form a simple, yet powerful variability mechanism that continuously learns, which variants to bind given the current context and system goals.

This report describes a work in progress to develop Autonomic Software Product Lines (ASPL). The ASPL is a dynamic software product line approach with a novel variability handling mechanism that enables traditional software product lines to adapt themselves at runtime in response to changes in their context, requirements and business goals. The ASPL variability mechanism is composed of three key activities: 1) context-profiling, 2) context-aware composition, and 3) online learning. Context-profiling is an offline activity that prepares a knowledge base for context-aware composition. The context-aware composition uses the knowledge base to derive a new product or adapts an existing product based on a product line's context attributes and goals. The online learning optimizes the knowledge base to remove errors and suboptimal information and to incorporate new knowledge. The three activities together form a simple yet powerful variability handling mechanism that learns and adapts a system at runtime in response to changes in system context and goals. We evaluated the ASPL variability mechanism on three small-scale software product lines and got promising results. The ASPL approach is, however, is yet at an initial stage and require improved development support with more rigorous evaluation. 

More than two decades of research have demonstrated an increasing need for software systems to be self-adaptive. Self-adaptation is required to deal with runtime dynamics which are difficult to predict before deployment. A vast body of knowledge to develop Self-Adaptive Software Systems (SASS) has been established. We, however, discovered a lack of process support to develop self-adaptive systems with reuse. To that end, we propose a domain-engineering based methodology, Autonomic Software Product Lines engineering (ASPLe), which provides step-by-step guidelines for developing families of SASS with systematic reuse. The evaluation results from a case study show positive effects on quality and reuse for self-adaptive systems designed using the ASPLe compared to state-of-the-art engineering practices.

We describe ongoing work in knowledge evolution management for autonomic software product lines. We explore how an autonomic product line may benefit from new knowledge originating from different source activities and artifacts at run time. The motivation for sharing run-time knowledge is that products may self-optimize at run time and thus improve quality faster compared to traditional software product line evolution. We propose two mechanisms that support knowledge evolution in product lines: online learning and knowledge sharing. We describe two basic scenarios for runtime knowledge evolution that involves these mechanisms. We evaluate online learning and knowledge sharing in a small product line setting that shows promising results.

The concept of variability is fundamental in software product lines and a successful implementation of a product line largely depends on how well domain requirements and their variability are specified, managed, and realized. While developing an educational software product line, we identified a lack of support to specify variability in quality concerns. To address this problem we propose an approach to model variability in quality concerns, which is an extension of quality attribute scenarios. In particular, we propose domain quality attribute scenarios, which extend standard quality attribute scenarios with additional information to support specification of variability and deriving product specific scenarios. We demonstrate the approach with scenarios for robustness and upgradability requirements in the educational software product line.

Software architecture is a software engineering discipline that

provides notations and processes for high-level partitioning of

systems' responsibilities early in the software design process. This

thesis is concerned with a specific subclass of systems, systems with a dynamic software architecture. They have practical applications in various domains such as high-availability systems and ubiquitous computing.

In a dynamic software architecture, the set of architectural elements and the configuration of these elements may change at run-time. These modifications are motivated by changed system requirements or by changed execution environments. The implications of change events may be the addition of new functionality or re-configuration to meet new Quality of Service requirements.

This thesis investigates new modeling and implementation techniques for dynamic software architectures. The field of Dynamic Architecture is surveyed and a common ground defined. We introduce new concepts and techniques that simplify understanding, modeling, and implementation of systems with a dynamic architecture, with this common ground as our starting point. In addition, we investigate practical use and reuse of quality implementations, where a dynamic software architecture is a

fundamental design principle.

The main contributions are a taxonomy, a classification, and a set of architectural patterns for dynamic software architecture. The taxonomy and classification support analysis, while the patterns affect design and implementation work directly. The investigation of practical applications of dynamic architectures identifies several issues concerned with use and reuse, and discusses alternatives and solutions where possible.

The results are based on surveys, case studies, and exploratory development of dynamic software architectures in different

application domains using several approaches. The taxonomy,

classification and architecture patterns are evaluated through several experimental prototypes, among others, a high-performance scientific computing platform.

In this paper, we discuss how for self-adaptive systems someactivities that traditionally occur at development-time are moved to runtime. Responsibilities for these activities shift from software engineers tothe system itself, causing the traditional boundary between development time and run-time to blur. As a consequence, we argue how the traditional  software engineering process needs to be reconceptualized to distinguishvboth development-time and run-time activities, and to support designers in taking decisions on how to properly engineer such systems.Furthermore, we identify a number of challenges related to this required reconceptualization, and we propose initial ideas based on process modeling.We use the Software and Systems Process Engineering Meta-Model(SPEM) to specify which activities are meant to be performed o-line andon-line, and also the dependencies between them. The proposed models should capture information about the costs and benets of shifting activitiesto run-time, since such models should support software engineers in their decisions when they are engineering self-adaptive systems.

The digitization of industry has drastically changed the competitive landscape by requiring a higher degree of specialization and shorter time to delivery that affect the design properties a software platform should satisfy. The platform architecture must sustain continuous and rapid change to the organizational architecture, which in turn is affected by external forces: i.e., forces drive the velocity of change. In this paper, we explore the effects of digitization, characterizing internal and external forces that impact on business strategies and trigger the continuous realignment of the platform, and outline a research agenda to mitigate the effects.

Architecting a self-adaptive system with decentralized control is challenging. Indeed, architects shall consider several different and interdependent design dimensions and devise multiple control loops to coordinate and timely perform the correct adaptations. To support this task, we propose Decor, a reasoning framework for architecting and evaluating decentralized control. Decor provides (i) multi-paradigm modeling support, (ii) a modeling environment for MAPE-K style decentralized control, and (iii) a co-simulation environment for simulating the decentralized control together with the managed system and estimating the quality attributes of interest. We apply the Decor in three case studies: an intelligent transportation system, a smart power grid, and a cloud computing application. The studies demonstrate the framework’s capabilities to support informed architectural decisions on decentralized control and adaptation strategies.

Software engineering projects are often used to teach complex aspects of software engineering. It is well-known that these courses are difficult to run. In this paper we address one aspect that can be used to reduce differences between projects and project groups to more easily find the balance of “challenging but possible”. We describe the role of the “puppeteer” and present four communication patterns that we use in an international software engineering project course.

This paper discusses on-going work in adaptive architectures concerning automatic adaptation rule derivation. Adaptation is rule-action based but deriving rules that meet the adaptation goals are tedious and error prone. We present an approach that uses model-driven derivation and training for automatically deriving adaptation rules, and exemplify this in an environment for scientific computing.

The resilience system property has become more and more relevant, mainly because of the increasing dependance on a rapidly growing number of software-intensive, complex, socio-technical systems, which are facing uncertainty about changes they are expected to experience during their life-cycle and ways to deal with them. Methodologies for the systematic design and validation of resilience for such systems are thus highly necessary, and require contributions from several different fields. This paper contributes to current resilience research by providing a conceptual framework intended to serve as a common ground for the development of such methodologies. Its main points are: the identification of the main categories of changes a system should face; a clear definition of the different facets of resilience one could want to achieve, expressed in terms of the system dynamics; a mapping of each of these facets to design strategies that are better suited to achieve it; and the corresponding identification of possible metrics that can be used to assess its achievement. 

In recent years, we have observed the increasing interest in the system property resilience. We ascribe this increasing interest to the rapidly growing number of deployed, complex, socio-technical systems, which are facing uncertainty about changes they are expected to experience during their life-cycle and ways to deal with them. This paper contributes to current resilience research by focusing on the different definitions given for this system property, highlighting the risk that, using different terms in different communities, this contributes to create a “tower of Babel” problem, with the consequent difficulty in exchanging ideas and working together towards a common goal. We adopt an extended definition of dependability to define resilience. Based on that, we identify features of resilient systems, capture properties falling under the resilience umbrella, and define a conceptual framework for resilience characterization including how changes affect the system, strategies to design resilience, and discuss metrics for quantifying resilience at design and runtime.

In a service-oriented setting, where services are composed to provide end user functionality, it is a challenge to find the service components with best-fit functionality and quality. A decision based on information mainly provided by service providers is inadequate as it cannot be trusted in general. In this paper, we discuss service compositions in an open market scenario where an automated best-fit service selection and composition is based on Service Level Achievements instead. Continuous monitoring updates the actual Service Level Achievements which can lead to dynamically changing compositions. Measurements of real life services exemplify the approach.

Context: Traditionally, Embedded Systems (ES) are tightly linked to physical products, and closed both for communication to the surrounding world and to additions or modifications by third parties. New technical solutions are however emerging that allow addition of plug-in software, as well as external communication for both software installation and data exchange. These mechanisms in combination will allow for the construction of Federated Embedded Systems (FES). Expected benefits include the possibility of third-party actors developing add-on functionality; a shorter time to market for new functions; and the ability to upgrade existing products in the field. This will however require not only new technical solutions, but also a transformation of the software ecosystems for ES. Objective: This paper aims at providing an initial characterization of the mechanisms that need to be present to make a FES ecosystem successful. This includes identification of the actors, the possible business models, the effects on product development processes, methods and tools, as well as on the product architecture. Method: The research was carried out as an explorative case study based on interviews with 15 senior staff members at 9 companies related to ES that represent different roles in a future ecosystem for FES. The interview data was analyzed and the findings were mapped according to the Business Model Canvas (BMC). Results: The findings from the study describe the main characteristics of a FES ecosystem, and identify the challenges for future research and practice. Conclusions: The case study indicates that new actors exist in the FES ecosystem compared to a traditional supply chain, and that their roles and relations are redefined. The business models include new revenue streams and services, but also create the need for trade-offs between, e.g., openness and dependability in the architecture, as well as new ways of working. (C) 2014 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.orgilicensesiby/3.0/).

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.

The ERASMUS+ European University for Well-Being (EUniWell) alliance’s mission aims to resolve the paradox of Europeans’ relative prosperity against the global security and sustainability challenge. “Maximising Academic and Social Outcomes in Engineering Education” is a project which interprets this contradiction for engineering educators; how to best teach non-technical skills to ensure engineers make the utmost contribution to societal wellbeing? Appreciably, the social outcome for the person who becomes an engineer is positive because the profession is relatively well-paid. Therefore, engineering education is good for social mobility providing the learning environment narrows attainment gaps between disadvantaged and mainstream cohorts. Accordingly, our strategy is to bring together the expertise of the British, French, Italian and Swedish faculties to transfer best practice for professional, business and sustainability skill teaching, while contrasting how their disadvantaged cohorts present. The project has two primary objectives: To understand how partners differ in terms of skill teaching, and how students from disadvantaged backgrounds are accommodated. The paper describes the background and rationale of the project, and its research design and methodology. Although the project is still in progress and data collection is still underway, this paper provides insights and perspectives for engineering educators looking to design similar collaborations to share best practice, while considering engineering identities and their underlying competencies. 

There is a notable discrepancy between the relative prosperity of Europeans and the global security and sustainability challenge. The mission of the ERASMUS+ 2020 European University for Well-Being (EUniWell) alliance is to address this. Our project, “Maximizing Academic and Social Outcomes in Engineering Education” (MASOEE) interprets this contradiction for engineering educators, exploring how to ensure graduates make the utmost contribution to societal wellbeing by narrowing attainment gaps. We are combining the expertise of British, French, Italian, and Swedish faculties to identify, share, and ultimately transfer best practices for professional, business, and sustainability skill teaching that is aligned to the EU competency frameworks including EntreComp (Bacigalupo et al. 2016) and GreenComp (Bianchi, Pisiotis, and Cabrera Giraldez 2022). Furthermore, we are finding out how disadvantaged cohorts in each partner faculty are characterized and supported. The project is guided by the following research questions:

• What are the similarities and differences between our students, staff, teaching, and culture?
• How are skills taught and embedded in programmes? What are student attitudes to learning these? How do we currently define and measure social outcomes?
• Which new approaches can we employ improve social and academic outcomes?

The MASOEE project brings together engineering faculties in the EUniWell alliance to share best practices for teaching transversal skills so that engineers contribute to societal well-being. The study combines the expertise of several engineering faculties at European universities. It focuses on sharing and developing expertise to improve the social outcomes of engineering students. Namely, researchers examine the similarities and differences between partners regarding their student bodies, teaching, programme structures, and institutions’ culture. Moreover, the work also explores how transversal skills are taught, what student attitudes are in terms of learning these skills, and how educators can better teach them.

The research design includes several activities across four work packages (WPs). To ensure that partners use the same skill descriptions, we use well-established organizations' existing definitions. WP1 strives to identify best practices within EUniWell based on the 15 entrepreneurial competencies defined in EU EntreComp Framework. WP2 targets engineering students' ability to solve complex challenges, communication, and networking skills defined in the "21st century skills" by the World Economic Forum. WP3 investigates the engineering schools’ capacity to train engineering students in sustainable competence, forming responsible engineers capable of developing sustainable solutions using the skills defined by the EU GreenComp. WP4 supports the other packages with engineering education research, specifically data collection and analysis, knowledge forming, and evaluation. The project runs from August 2022 until September 2023.  

The MASOEE project partners gather knowledge within their organisations through joint surveys and focus groups and collectively identify and share best practices. The engineering identity, taught as transversal skills by participating partners, can evolve from a traditional technologist identity along three paths: the self-made engineer, the progressive technologist, and the responsible engineer. By sharing best practices for teaching these skills, we believe we will better understand what the future engineer - who integrates all three identities – will be.

The goal of this roadmap paper is to summarize the state-of-the-art and identify research challenges when developing, deploying and managing self-adaptive software systems. Instead of dealing with a wide range of topics associated with the field, we focus on four essential topics of self-adaptation: design space for self-adaptive solutions, software engineering processes for self-adaptive systems, from centralized to decentralized control, and practical run-time verification & validation for self-adaptive systems. For each topic, we present an overview, suggest future directions, and focus on selected challenges. This paper complements and extends a previous roadmap on software engineering for self-adaptive systems published in 2009 covering a different set of topics, and reflecting in part on the previous paper. This roadmap is one of the many results of the Dagstuhl Seminar 10431 on Software Engineering for Self-Adaptive Systems, which took place in October 2010.

Context: Testing techniques proposed in the literature rely on various sources of information for test case selection (e.g., requirements, source code, system structure, etc.). The challenge of test selection is amplified in the context of heterogeneous systems, where it is unknown which information/data sources are most important.

Contribution: (1) Achieve in-depth understanding of test processes in heterogeneous systems; (2) Elicit information sources for test selection in the context of heterogeneous systems. (3) Capture the relative importance of the identified information sources.

Method: Case study research is used for the elicitation and understanding of which information sources are relevant for test case privatization, followed by an exploratory survey capturing the relative importance of information sources for testing heterogeneous systems.

Results: We classified different information sources that play a vital role in the test selection process, and found that their importance differs largely for the different test levels observed in heterogeneous testing. However, overall all sources were considered essential in test selection for heterogeneous systems.

Conclusion: Heterogeneous system testing requires solutions that take all information sources into account when suggesting test cases for selection. Such approaches need to be developed and compared with existing solutions.

The use of multiple mobile devices is increasing in mobile learning, bringing a need for collaboration and resource sharing among participating pupils. This paper presents an approach that addresses information and resource sharing for mobile devices in indoors and outdoors settings. Our solution consists of aggregated mobile devices, forming organizations. These Mobile Virtual Devices (MVDs) provide a new mechanism that facilitates design of mobile learning activities offering a virtual complex device that combines the features of several mobile devices.

The increasing use of mobile devices to support collaborative activities creates a need for developing new methods and perspectives to facilitate information sharing. In this paper, we present an approach for information sharing in mobile collaborative settings through the use of Mobile Virtual Devices (MVD). MVD emerges as a new conceptualization of an organization of mobile devices that supports collaborative tasks. The use of MVD allows designers and users to interact with and through mobile devices in novel ways, considering the aggregation of mobile devices as a single entity. The notion of MVD has been conceptualized on the idea of multirole devices, using components to provide and consume resources.

Through the analysis of the different iterations of the Geometry Mobile (GEM) project, a mobile learning effort in the field of mathematics, we have identified a major architectural issue to be addressed in the design and implementation of m-learning applications. Due to the dynamic nature of the field many challenging requirements are continuously emerging. One of them relates to the possibility to support collaborative activities that demand sharing resources between students and their mobile devices in constantly changing conditions. These situations generate the need of using decentralized distributed architectures in which mobile devices can share resources to carry out the activity covering the concerns defined by the different stakeholders. This paper describes our current efforts connected to identifying a set of requirements for M-Learning activities. Thereafter, we elaborate on why a decentralized distributed system (DDS) can be used to provide a novel solution to tackle the mentioned above problems. Moreover, initial aspects related to the design of a DDS, including a self-adaptation mechanism are presented.

The use of collaborative activities in education has proven to be an effective way to enhance students’ learning outcomes by in- creasing their engagement and motivating discussions on the learning topics under exploration. In the field of Technology Enhanced Learn- ing (TEL), the use of information and communication technologies has been extensively studied to provide alternative methods to support col- laborative learning activities, combining different applications and tools. Mobile learning, a subset of TEL, has become a prominent area of re- search as it offers promising tools to enhance students’ collaboration and it provides alternative views for teaching and learning subject matter in relevant and authentic scenarios. While many studies have focused on the pedagogical opportunities provided by mobile technologies, fewer are the efforts looking at technological related aspects. Hardware and software issues in this field still remain as challenges that require a deeper level of study and analysis. This paper presents and discusses the findings of a deep analysis based on the outcomes of three mobile collaborative learning activities and their requirements. These results have helped us to identify a number of arising challenges that need to be addressed in order to warranty Quality of Service (QoS) in these collaborative M- learning activities. Moreover, the paper offers a view on current practices in M-learning activities, which evidences the lack of research addressing software engineering aspects in mobile collaborative learning. 

This paper surveys work on ecosystems and open innovation of systems in the context of software engineering for embedded systems. The primary research goal is to develop a research agenda based on the topics identified within the research publications on the topic. The agenda is based on a systematic mapping study of 260 publications obtained from digital libraries and is influenced by a set of areas of interest, i.e., product lines, open source, third party, business models, open innovation, and strategy. The results from the study include analysis of the type of research conducted in the field, its origin and research contribution. The study identifies the need for more solutions to specific open innovation problems such as mapping business models to technical platforms; defining open ecosystem processes that foster open innovation; and improving how ecosystem players can leverage on tool support for open innovation. A direction for future research is also provided.

This paper discusses how Systems of Systems (SoS) can be constructed by linking together embedded computers in constituent systems to create complex but more flexible and adaptable systems. The approach of software system development is called Federated Embedded Systems (FES) and their revolved ecosystem of players is presented, aiming to ensure quality in engineering SoS. Ecosystems for Federated Embedded Systems (EcoFES) comprise a new area of research that scales component-based software development for embedded software into new dimensions. The proposed ecosystem dimension introduces an open, flexible and adaptable SoS architecture for improving the process of FES development. In the paper, we identify some architectural challenges and discuss the implications of scaling from a closed ecosystem to an open one, providing open collaboration and innovation in the context of FES.

Software ecosystems is one promising strategy for organizations to find new market segments, new innovative value propositions creating new value streams. However, understanding internal and external actors, resources and relationships that could be leveraged in a SECO is critical for their strategic decisions. The consequence of mistakes may be costly failures that can force an organization to move out of a market. This paper describes a systematic mapping study that targets description of software ecosystems. Our conjecture is that adequate description support leads to modeling, which will improve information and in turn strategic decisions. The survey searches existing literature for description techniques and their application for comprehensive description. The study identifies and maps 63 primary studies out of 937 candidates according to their degree of modeling support and several other important aspects for SECO description. The analysis indicates that no approach fully supports comprehensive SECO descriptions, supporting domain specific and view specific modeling of ecosystem concerns. The analysis is used to highlight areas for a future research agenda.

The increased use of information and communication technologies (ICT) in schools promises up-to-date, interactive and collaborative learning content, However, this has proved difficult to fulfill as the requirements from students and teachers combined with devices in a variety of contexts are expensive to meet. Software reuse is a proven way to decrease development time, and thus promises a way to decrease these costs of ICT. This paper explores the characteristics of a software ecosystem approach to cater for the new digital school and presents a reference model for ecosystems developed for the domain of technology-enhanced learning (TEL).

Software Ecosystems (SECOs) have in recent years emerged as a promising approach for improved inter and intra organizational reuse and for reusability involving end-users in the software development process. Understanding the software's role in a larger perspective and how it interconnects with stakeholders is key for progress in those directions. However, for SECOs, several theoretical and methodological gaps remain to be charted. This paper identifies one such gap, the need for precise process modeling. It elaborates on experiences gained from the analysis of a SECO for mobile learning and brings up several aspects and insights for this particular domain. The main contributions are an initial reference model for the mobile learning domain and an outline for an analysis method for domain specific SECOs.

Self-adaptive software systems adapt to changes in the environment, in the system itself, in their requirements, or in their business objectives. Typically, these systems attempt to maintain system goals at run time and often provide assurance that they will meet their goals under dynamic and uncertain circumstances. While significant research has focused on ways to engineer self-adaptive capabilities into both new and legacy software systems, less work has been conducted on how to assure that self-adaptation maintains system goals. For traditional, especially safety-critical software systems, assurance techniques decompose assurances into sub-goals and evidence that can be provided by parts of the system. Existing approaches also exist for composing assurances, in terms of composing multiple goals and composing assurances in systems of systems. While some of these techniques may be applied to self-adaptive systems, we argue that several significant challenges remain in applying them to self-adaptive systems in this chapter. We discuss how existing assurance techniques can be applied to composing and decomposing assurances for self-adaptive systems, highlight the challenges in applying them, summarize existing research to address some of these challenges, and identify gaps and opportunities to be addressed by future research. © Springer International Publishing AG 2017.

Modern software systems are decentralized, distributed, and dynamic, and consequently, require decentralized mechanisms to enforce security. In this paper, we propose an adaptive approach using a combination of decentralized information flow control (DIFC) mechanisms, trust-based methods and decentralized control architectures to enforce security in open distributed systems. In our approach, adaptivity mitigates two aspects of the system dynamics that cause uncertainty: the ever-changing nature of trust and system openness. We formalize our trust-aware DIFC model and instantiate two decentralized control architectures to implement and evaluate it. We evaluate the effectiveness and performance of our method and decentralized control architectures on two case studies.

Modern software systems and their corresponding architectures are increasingly decentralized, distributed, and dynamic. As a consequence, decentralized mechanisms are required to ensure security in such architectures. Decentralized Information Flow Control (DIFC) is a mechanism to control information flow in distributed systems. This article presents and discusses several improvements to an adaptive decentralized information flow approach that incorporates trust for decentralized systems to provide security. Adaptive Trust-Aware Decentralized Information Flow (AT-DIFC+) combines decentralized information flow control mechanisms, trust-based methods, and decentralized control architectures to control and enforce information flow in an open, decentralized system. We strengthen our approach against newly discovered attacks and provide additional information about its reconfiguration, decentralized control architectures, and reference implementation. We evaluate the effectiveness and performance of AT-DIFC+ on two case studies and perform additional experiments and to gauge the mitigations’ effectiveness against the identified attacks.

Modern software systems and their corresponding architectures are decentralized, distributed, and dynamic. As a consequence, decentralized mechanisms are also required to ensure security in such architectures. Decentralized Information Flow Control (DIFC) is a mechanism to control information flow in distributed systems. However, DIFC mechanisms require the resolution of specific centralized control and trust issues.In this paper, we propose an adaptive, trust-aware, decentralized information flow approach that incorporates trust in DIFC for decentralized systems. We employ decentralized feedback loops to enable decentralized control and adaptive trust assignments. In our approach, adaptivity mitigates two aspects of systems dynamics that cause uncertainty:  the ever-changing nature of trust and the system openness. We formalize our trust-aware DIFC model and instantiate two decentralized feedback loop architectures to implement it.

Recent publications indicate the importance of software engineering in the computer science curriculum. In this paper, we present the final part of software engineering education at University of Groningen in the Netherlands and Växjö University in Sweden, where student teams perform an industrial software development project. It furthermore presents the main educational problems encountered in such real-life projects and explains how this international course addresses these problems. The main contribution of this paper is a set of seven good practices for project based software engineering education.

Engineering long-running computing systems that achieve their goals under ever-changing conditions pose significant challenges. Self-adaptation has shown to be a viable approach to dealing with changing conditions. Yet, the capabilities of a self-adaptive system are constrained by its operational design domain (ODD), i.e., the conditions for which the system was built (requirements, constraints, and context). Changes, such as adding new goals or dealing with new contexts, require system evolution. While the system evolution process has been automated substantially, it remains human-driven. Given the growing complexity of computing systems, human-driven evolution will eventually become unmanageable. In this paper, we provide a definition for ODD and apply it to a self-adaptive system. Next, we explain why conditions not covered by the ODD require system evolution. Then, we outline a new approach for self-evolution that leverages the concept of ODD, enabling a system to evolve autonomously to deal with conditions not anticipated by its initial ODD. We conclude with open challenges to realise self-evolution.

A system of systems (SoS) integrates independently useful systems into a larger system. Examples are integrated surveillance systems and networked smart homes. A SoS offers functions to users that cannot be provided by its individual parts, but emerge as a combination of these. However, providing these functions with a required level of quality is difficult due to inherent uncertainties, such as systems that attach and detach at will and faults that are difficult to predict. Self-adaptation is a well-studied approach that enables a system to reason about itself and adapt to achieve particular quality objectives in the face of uncertainties and change. However, the inherently decentralized nature of SoS raises fundamental challenges to self-adaptation. This paper presents three architectural styles to realize self-adaptation in SoS, discusses key challenges for each style, and outlines starting points that could help to tackle these challenges.

With the advancing digitisation of society and industry we observe a progressing blending of computational, physical, and social processes. The trustworthiness and sustainability of these systems will be vital for our society. However, engineering modern computing systems is complex as they have to: i) operate in uncertain and continuously changing environments, ii) deal with huge amounts of data, and iii) require seamless interaction with human operators. To that end, we argue that both systems and the way we engineer them must become smarter. With smarter we mean that systems and engineering processes adapt and evolve themselves through a perpetual process that continuously improves their capabilities and utility to deal with the uncertainties and amounts of data they face. We highlight key engineering areas: cyber-physical systems, self-adaptation, data-driven technologies, and visual analytics, and outline key challenges in each of them. From this, we propose a research agenda for the years to come.