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| 2. |
- Klima, Matej, et al.
(författare)
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Selected Code-Quality Characteristics and Metrics for Internet of Things Systems
- 2022
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Ingår i: IEEE Access. - : IEEE. - 2169-3536. ; 10, s. 46144-46161
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Tidskriftsartikel (refereegranskat)abstract
- Software code is present on multiple levels within current Internet of Things (IoT) systems. The quality of this code impacts system reliability, safety, maintainability, and other quality aspects. In this paper, we provide a comprehensive overview of code quality-related metrics, specifically revised for the context of IoT systems. These metrics are divided into main code quality categories: Size, redundancy, complexity, coupling, unit test coverage and effectiveness, cohesion, code readability, security, and code heterogeneity. The metrics are then linked to selected general quality characteristics from the ISO/IEC 25010:2011 standard by their possible impact on the quality and reliability of an IoT system, the principal layer of the system, the code levels and the main phases of the project to which they are relevant. This analysis is followed by a discussion of code smells and their relation to the presented metrics. The overview presented in the paper is the result of a thorough analysis and discussion of the author’s team with the involvement of external subject-matter experts in which a defined decision algorithm was followed. The primary result of the paper is an overview of the metrics accompanied by applicability notes related to the quality characteristics, the system layer, the level of the code, and the phase of the IoT project.
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| 3. |
- Provoost, Michiel, et al.
(författare)
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Joint Learning : A Pattern for Efficient Decision-Making and Reliable Communication in Self-Adaptive Internet of Things
- 2023
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Ingår i: EuroPLoP '23: Proceedings of the 28th European Conference on Pattern Languages of Programs, 5 July 2023. - : ACM Publications. - 9798400700408
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Konferensbidrag (refereegranskat)abstract
- An Internet-of-Things (IoT) system typically comprises many small computing elements (nodes) that are battery-powered and communicate over a wireless network. These elements monitor properties in the environment and send the data to client applications via gateways. The wireless networks used by the elements are subject to uncertainties that are difficult to predict upfront, such as dynamic objects (swaying trees, cars, …) and changing weather conditions that may deteriorate the transmissions. To ensure reliable communication over a wireless network of energy-constrained elements, recent research has proposed self-adaptive IoT systems. Such a self-adaptive system equips the network of elements – referred to as the managed system – with a feedback loop – the managing system. The managing system monitors the changing conditions and adapts the transmission settings of the IoT network to ensure the system’s quality goals. Leveraging and consolidating the existing knowledge in this area, we present a pattern that we coined Joint Learning that provides a solution to the decision-making problem of large, distributed self-adaptive IoT systems. With this pattern, elements use a joint learner to make adaptation decisions for individual elements while yielding reliable communication of the overall network. The pattern is applied to two cases to show that the solutions realize the system goals while operating under uncertainties.
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| 4. |
- Topfer, Michal, et al.
(författare)
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Online ML Self-adaptation in Face of Traps
- 2023
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Ingår i: Proceedings - 2023 IEEE International Conference on Autonomic Computing and Self-Organizing Systems, ACSOS 2023. - : IEEE. - 9798350337440 ; , s. 57-66
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Konferensbidrag (refereegranskat)abstract
- Online machine learning (ML) is often used in selfadaptive systems to strengthen the adaptation mechanism and improve the system utility. Despite such benefits, applying online ML for self-adaptation can be challenging, and not many papers report its limitations. Recently, we experimented with applying online ML for self-adaptation of a smart farming scenario and we had faced several unexpected difficulties - traps - that, to our knowledge, are not discussed enough in the community. In this paper, we report our experience with these traps. Specifically, we discuss several traps that relate to the specification and online training of the ML-based estimators, their impact on selfadaptation, and the approach used to evaluate the estimators. Our overview of these traps provides a list of lessons learned, which can serve as guidance for other researchers and practitioners when applying online ML for self-adaptation.
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| 5. |
- Weyns, Danny, et al.
(författare)
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Preliminary Results of a Survey on the Use of Self-Adaptation in Industry
- 2022
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Ingår i: Proceedings - 17th Symposium on Software Engineering for Adaptive and Self-Managing Systems, SEAMS 2022. - New York, NY, USA : IEEE. - 9781450393058 ; , s. 70-76
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Konferensbidrag (refereegranskat)abstract
- Self-Adaptation equips a software system with a feedback loop that automates tasks that otherwise need to be performed by operators. Such feedback loops have found their way to a variety of practical applications, one typical example is an elastic cloud. Yet, the state of the practice in self-Adaptation is currently not clear. To get insights into the use of self-Adaptation in practice, we are running a largescale survey with industry. This paper reports preliminary results based on survey data that we obtained from 113 practitioners spread over 16 countries, 62 of them work with concrete self-Adaptive systems. We highlight the main insights obtained so far: motivations for self-Adaptation, concrete use cases, and difficulties encountered when applying self-Adaptation in practice. We conclude the paper with outlining our plans for the remainder of the study. © 2022 ACM.
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| 6. |
- Weyns, Danny, et al.
(författare)
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Self-Adaptation in Industry : A Survey
- 2023
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Ingår i: ACM Transactions on Autonomous and Adaptive Systems. - : ACM Publications. - 1556-4665 .- 1556-4703. ; 18:2
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Tidskriftsartikel (refereegranskat)abstract
- Computing systems form the backbone of many areas in our society, from manufacturing to traffic control, healthcare, and financial systems. When software plays a vital role in the design, construction, and operation, these systems are referred to as software-intensive systems. Self-adaptation equips a software-intensive system with a feedback loop that either automates tasks that otherwise need to be performed by human operators or deals with uncertain conditions. Such feedback loops have found their way to a variety of practical applications; typical examples are an elastic cloud to adapt computing resources and automated server management to respond quickly to business needs. To gain insight into the motivations for applying self-adaptation in practice, the problems solved using self-adaptation and how these problems are solved, and the difficulties and risks that industry faces in adopting self-adaptation, we performed a large-scale survey. We received 184 valid responses from practitioners spread over 21 countries. Based on the analysis of the survey data, we provide an empirically grounded overview the of state of the practice in the application of self-adaptation. From that, we derive insights for researchers to check their current research with industrial needs, and for practitioners to compare their current practice in applying self-adaptation. These insights also provide opportunities for applying self-adaptation in practice and pave the way for future industry-research collaborations.
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| 7. |
- Weyns, Danny, et al.
(författare)
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Six Software Engineering Principles for Smarter Cyber-Physical Systems
- 2021
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Ingår i: Proceedings of the 2021 IEEE International Conference on Autonomic Computing and Self-Organizing Systems Companion (ACSOS-C). - : IEEE. - 9781665443937 - 9781665443944 ; , s. 198-203
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Konferensbidrag (refereegranskat)abstract
- Cyber-Physical Systems (CPS) integrate computational and physical components. With the digitisation of society and industry and the progressing integration of systems, CPS need to become 'smarter' in the sense that they can adapt and learn to handle new and unexpected conditions, and improve over time. Smarter CPS present a combination of challenges that existing engineering methods have difficulties addressing: intertwined digital, physical and social spaces, need for heterogeneous modelling formalisms, demand for context-tied cooperation to achieve system goals, widespread uncertainty and disruptions in changing contexts, inherent human constituents, and continuous encounter with new situations. While approaches have been put forward to deal with some of these challenges, a coherent perspective on engineering smarter CPS is lacking. In this paper, we present six engineering principles for addressing the challenges of smarter CPS. As smarter CPS are software-intensive systems, we approach them from a software engineering perspective with the angle of self-adaptation that offers an effective approach to deal with run-time change. The six principles create an integrated landscape for the engineering and operation of smarter CPS.
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| 8. |
- Weyns, Danny, et al.
(författare)
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Towards a Research Agenda for Understanding and Managing Uncertainty in Self-Adaptive Systems
- 2023
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Ingår i: Software Engineering Notes. - : ACM Publications. - 0163-5948 .- 1943-5843. ; 48:4, s. 20-36
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Tidskriftsartikel (refereegranskat)abstract
- Despite considerable research efforts on handling uncertainty in self-adaptive systems, a comprehensive understanding of the precise nature of uncertainty is still lacking. This paper summarises the findings of the 2023 Bertinoro Seminar on Uncertainty in Self- Adaptive Systems, which aimed at thoroughly investigating the notion of uncertainty, and outlining open challenges associated with its handling in self-adaptive systems. The seminar discussions were centered around five core topics: (1) agile end-toend handling of uncertainties in goal-oriented self-adaptive systems, (2) managing uncertainty risks for self-adaptive systems, (3) uncertainty propagation and interaction, (4) uncertainty in self-adaptive machine learning systems, and (5) human empowerment under uncertainty. Building on the insights from these discussions, we propose a research agenda listing key open challenges, and a possible way forward for addressing them in the coming years.
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