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- de Lemos, Rogerio, et al.
(författare)
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Software Engineering for Self-Adaptive Systems : A Second Research Roadmap
- 2013
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Ingår i: Software Engineering for Self-Adaptive Systems II. - Berlin, Heidelberg : Springer. - 9783642358128 ; , s. 1-32
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- 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.
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- 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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- 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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- Weyns, Danny, et al.
(författare)
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Towards Better Adaptive Systems by Combining MAPE, Control Theory, and Machine Learning
- 2021
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Ingår i: Proceedings of the 2021 International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS). - : IEEE. - 9781665402897 - 9781665402903 ; , s. 217-223
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Konferensbidrag (refereegranskat)abstract
- Two established approaches to engineer adaptive systems are architecture-based adaptation that uses a Monitor-Analysis-Planning-Executing (MAPE) loop that reasons over architectural models (aka Knowledge) to make adaptation decisions, and control-based adaptation that relies on principles of control theory (CT) to realize adaptation. Recently, we also observe a rapidly growing interest in applying machine learning (ML) to support different adaptation mechanisms. While MAPE and CT have particular characteristics and strengths to be applied independently, in this paper, we are concerned with the question of how these approaches are related with one another and whether combining them and supporting them with ML can produce better adaptive systems. We motivate the combined use of different adaptation approaches using a scenario of a cloud-based enterprise system and illustrate the analysis when combining the different approaches. To conclude, we offer a set of open questions for further research in this interesting area.
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