# Perpetual Assurances for Self-Adaptive Systems

arXiv: https://arxiv.org/abs/1903.04771 · HTML: https://mlsanity.com/paper/1903.04771/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Mar 2019.

## Built on by (most cited first, 9 of 9)

- [Explaining Online Reinforcement Learning Decisions of Self-Adaptive Systems](https://mlsanity.com/paper/2210.05931/) (2022)
- [On the Impact of Applying Machine Learning in the Decision-Making of Self-Adaptive Systems](https://mlsanity.com/paper/2103.10194/) (2021)
- [The Vision of Self-Evolving Computing Systems](https://mlsanity.com/paper/2204.06825/) (2022)
- [A User Study on Explainable Online Reinforcement Learning for Adaptive Systems](https://mlsanity.com/paper/2307.04098/) (2023)
- [Deep Learning for Effective and Efficient Reduction of Large Adaptation Spaces in Self-Adaptive Systems](https://mlsanity.com/paper/2204.06254/) (2022)
- [Safe AI -- How is this Possible?](https://mlsanity.com/paper/2201.10436/) (2022)
- [A Subjective Logic-based method for runtime confidence updates in safety arguments](https://mlsanity.com/paper/2605.22530/) (2026)
- [Reality Is the Final Verifier: On Two Key Gaps in Agentic Software Engineering](https://mlsanity.com/paper/2609.12039/) (2026)
- [Assurance Envelopes for Autonomous Coding Agents: Minimum-Cost Evidence for Software Change](https://mlsanity.com/paper/2609.16302/) (2026)

## 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.