Perpetual Assurances for Self-Adaptive Systems
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Abstract (arXiv)
Providing assurances for self-adaptive systems is challenging. A primary underlying problem is uncertainty that may stem from a variety of different sources, ranging from incomplete knowledge to sensor noise and uncertain behavior of humans in the loop. Providing assurances that the self-adaptive system complies with its requirements calls for an enduring process spanning the whole lifetime of the system. In this process, humans and the system jointly derive and integrate new evidence and arguments, which we coined perpetual assurances for self-adaptive systems. In this paper, we provide a background framework and the foundation for perpetual assurances for self-adaptive systems. We elaborate on the concrete challenges of offering perpetual assurances, requirements for solutions, realization techniques and mechanisms to make solutions suitable. We also present benchmark criteria to compare solutions. We then present a concrete exemplar that researchers can use to assess and compare approaches for perpetual assurances for self-adaptation.
Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). First posted on arXiv 12 Mar 2019.
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Citing papers per year: 2021: 1; 2022: 4; 2023: 1; 2024: 0; 2025: 0; 2026: 3.
9 papers.
- 2022Explaining Online Reinforcement Learning Decisions of Self-Adaptive Systemscited by 5
- 2021On the Impact of Applying Machine Learning in the Decision-Making of Self-Adaptive Systemscited by 4
- 2022The Vision of Self-Evolving Computing Systemscited by 4
- 2023A User Study on Explainable Online Reinforcement Learning for Adaptive Systemscited by 3
- 2022Deep Learning for Effective and Efficient Reduction of Large Adaptation Spaces in Self-Adaptive Systemscited by 2
- 2022Safe AI -- How is this Possible?cited by 0
- 2026A Subjective Logic-based method for runtime confidence updates in safety argumentscited by 0
- 2026Reality Is the Final Verifier: On Two Key Gaps in Agentic Software Engineeringcited by 0
- 2026Assurance Envelopes for Autonomous Coding Agents: Minimum-Cost Evidence for Software Changecited by 0
- 2026Assurance Envelopes for Autonomous Coding Agents: Minimum-Cost Evidence for Software Change14 Sep 2026
- 2026Reality Is the Final Verifier: On Two Key Gaps in Agentic Software Engineering10 Sep 2026
- 2026A Subjective Logic-based method for runtime confidence updates in safety arguments21 May 2026
- 2023A User Study on Explainable Online Reinforcement Learning for Adaptive Systems9 Jul 2023
- 2022Explaining Online Reinforcement Learning Decisions of Self-Adaptive Systems12 Oct 2022
- 2022The Vision of Self-Evolving Computing Systems14 Apr 2022
- 2022Deep Learning for Effective and Efficient Reduction of Large Adaptation Spaces in Self-Adaptive Systems13 Apr 2022
- 2022Safe AI -- How is this Possible?25 Jan 2022
- 2021On the Impact of Applying Machine Learning in the Decision-Making of Self-Adaptive Systems18 Mar 2021