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Quality requirements deal with how well a product should perform the intended functionality, such as start-up time and learnability. Researchers argue they are important and at the same time studies indicate there are deficiencies in practice. Our goal is to review the state of evidence for quality requirements. We want to understand the empirical research on quality requirements topics as well as evaluations of quality requirements solutions. We used a hybrid method for our systematic literature review. We defined a start set based on two literature reviews combined with a keyword-based search from selected publication venues. We snowballed based on the start set. We screened 530 papers and included 84 papers in our review. Case study method is the most common (43), followed by surveys (15) and tests (13). We found no replication studies. The two most commonly studied themes are (1) differentiating characteristics of quality requirements compared to other types of requirements, (2) the importance and prevalence of quality requirements. Quality models, QUPER, and the NFR method are evaluated in several studies, with positive indications. Goal modeling is the only modeling approach evaluated. However, all studies are small scale and long-term costs and impact are not studied. We conclude that more research is needed as empirical research on quality requirements is not increasing at the same rate as software engineering research in general. We see a gap between research and practice. The solutions proposed are usually evaluated in an academic context and surveys on quality requirements in industry indicate unsystematic handling of quality requirements.

With the emergence of "Industry 4.0", the integration of cloud technologies and industrial cyber-physical systems becomes increasingly important to boost productivity. The industrial cyber-physical systems infrastructures and their fusion with the cloud lead to massive amounts of data acquired for controlling particular processes, but also for supporting decision-making. Although cloud-assisted systems are increasingly important in many domains, and ensuring their dependability is crucial, existing platforms do not provide satisfactory support to meet the dependability demands of industrial applications. The overall goal of the ACICS project is to provide models, methods and tools that facilitate a substantial increase of dependability of cloud-based platforms for industrial cyber-physical applications, with respect to consistency, security and interoperability of data, timing predictability of using shared virtual resources, together with a framework of guaranteeing quality-of-service enforcement by formal analysis and verification. In this paper, we present the main conceptual ideas behind the ACICS approach.

The rapid development of Industry 4.0 and Industrial Cyber-Physical Systems is leading to the exponential growth of unprocessed volumes of data. Industrial cloud computing has great potential for providing the resources for processing this data. To be widely adopted, the cloud must ensure satisfactory levels of Qualityof Service (QoS). However, the lack of a standardized model of quality attributes hinders the assessmentof QoS levels. This paper provides a comprehensive systematically defined map of current research trends,results, and gaps in quality attributes and QoS in industrial cloud computing. An extract of the main insights is as follows: (i) the adoption of cloud technologies is closely related to performance indicators, however other quality attributes, such as security, are not considered as much as they should; (ii) solutions are most often not tailored to specific industrial application domains; (iii) research largely focuses on providing solutions with outsolid validation, unsuitable for effective and fruitful technology transfer.

Software engineering is decision intensive. Evidence-based software engineering is suggested for decision-making concerning the use of methods and technologies when developing software. Software development often includes the reuse of software assets, for example, open-source components. Which components to use have implications on the quality of the software (e.g., maintainability). Thus, research is needed to support decision-making for composite software. This paper presents a roadmap for research required to support evidence-based decision-making for choosing and integrating assets in composite software systems. The roadmap is developed as an output from a 5-year project in the area, including researchers from three different organizations. The roadmap is developed in an iterative process and is based on (1) systematic literature reviews of the area; (2) investigations of the state of practice, including a case survey and a survey; and (3) development and evaluation of solutions for asset identification and selection. The research activities resulted in identifying 11 areas in need of research. The areas are grouped into two categories: areas enabling evidence-based decision-making and those related to supporting the decision-making. The roadmap outlines research needs in these 11 areas. The research challenges and research directions presented in this roadmap are key areas for further research to support evidence-based decision-making for composite software.

The cloud computing paradigm provides remote computing resources to the cloud service consumers and businesses. When combined with Internet of Things (IoT), both technologies open up a wide range of new possibilities for more agile and flexible applications. However, guaranteed quality of service is essential in provisioning of cloud services, which makes Service Level Agreements (SLAs) a focal point in the cloud computing and IoT ecosystem. The SLA definition and modeling phase is crucial in establishing SLAs between service providers and consumers. This paper identifies that the research on definition and modeling of SLAs for cloud services in IoT is widely dispersed and there is a lack of a systematic and comprehensive literature review. Thus, in this paper we build on top of a previously conducted systematic mapping study on management of SLAs for cloud computing and IoT to perform a comprehensive systematic review and discuss sub-categorization of the definition and modeling aspects of SLAs for cloud services in IoT. Furthermore we analyze the extracted relevant literature, present commonalities in the studies, identify gaps and discuss opportunities for further research in the area. 

One pressing challenge of many modern embedded systems is to successfully deal with the considerable amount of data that originates from the interaction with the environment. A recent solution comes from the use of GPUs, providing a significantly improved performance for data-parallel applications. Another trend in the embedded systems domain is component-based development. However, existing component-based approaches lack specific support to develop embedded systems with GPUs. As a result, components with GPU capability need to encapsulate all the required GPU information, leading to component specialization to specific platforms, hence drastically impeding component reusability. To facilitate component-based development of embedded systems with GPUs, we introduce the concept of flexible components. This increases the design flexibility by allowing the system developer to decide component allocation (i.e., either the CPU or GPU) at a later stage of the system development, with no change to the component implementation. Furthermore, we provide means to automatically generate code for adapting flexible components corresponding to their hardware placement, as well as code for component communication. Through the introduced support, components with GPU capability are platform-independent, and can be executed, without manual adjustment, on a large variety of hardware (i.e., platforms with different GPU characteristics).

New trends in software engineering recently emerged to cope with even more complex systems which in turns highlight problems software shortfalls and defects. The SQUADE (Software QUAlities and their DEpendencies) workshop focuses on increasing the understanding of the nature of Software Qualities (SQs), i.e., nonfunctional properties or extra-functional requirements (e.g., reliability, security, maintainability, etc.), and their interrelationships with the aim of bringing them into practice of software engineering. The topic is highly relevant due to the current trend of designing and developing software-intensive systems with larger complexity, increased autonomy, higher speed of changes, and growing need for interoperability within systems of systems. Unfortunately, this new trend comes with more software shortfalls and defects, which are widely and publicly spread. The primary goal of the workshop is to bring together researchers and practitioners to build more solid foundations when dealing with software qualities.

Platforms equipped with GPU processors help mitigating the ever-increasing computational demands of modern embedded systems. Such systems can be specifically developed by using component-based development thanks to the concept of flexible components. Through this concept, a component can be transparently executed either on a CPU or a GPU. However, this flexibility complicates the allocation process because it adds additional complexity (i.e., due to the undecided CPU or GPU execution) and constraints to consider (i.e., CPUs and GPUs properties). In this work, we address this problem by providing an optimization model for component-based embedded systems executing on both CPU and GPU. The model addresses important optimization goals, characteristic to the embedded system domain, such as memory usage, energy usage and execution time. A novelty of this work is the formal description of the optimization model, which supports the usage of mixed integer nonlinear programming to compute optimal allocation schemes. To examine the feasibility of the proposed method, we apply the optimization model on a vision system constructed using the industrial Rubus component model.

The choice of which software component to use influences the success of a software system. Only a few empirical studies investigate how the choice of components is conducted in industrial practice. This is important to understand to tailor research solutions to the needs of the industry. Existing studies focus on the choice for off-the-shelf (OTS) components. It is, however, also important to understand the implications of the choice of alternative component sourcing options (CSOs), such as outsourcing versus the use of OTS. Previous research has shown that the choice has major implications on the development process as well as on the ability to evolve the system. The objective of this study is to explore how decision making took place in industry to choose among CSOs. Overall, 22 industrial cases have been studied through a case survey. The results show that the solutions specifically for CSO decisions are deterministic and based on optimization approaches. The non-deterministic solutions proposed for architectural group decision making appear to suit the CSO decision making in industry better. Interestingly, the final decision was perceived negatively in nine cases and positively in seven cases, while in the remaining cases it was perceived as neither positive nor negative.