# Engineering Trustworthy Self-Adaptive Software with Dynamic Assurance Cases

arXiv: https://arxiv.org/abs/1703.06350 · HTML: https://mlsanity.com/paper/1703.06350/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Mar 2017.

## Built on by (most cited first, 14 of 14)

- [Reliability Assessment and Safety Arguments for Machine Learning Components in System Assurance](https://mlsanity.com/paper/2112.00646/) (2021)
- [Safety Analysis in the Era of Large Language Models: A Case Study of STPA using ChatGPT](https://mlsanity.com/paper/2304.01246/) (2023)
- [Bayesian Learning for the Robust Verification of Autonomous Robots](https://mlsanity.com/paper/2303.08476/) (2023)
- [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)
- [Towards Better Adaptive Systems by Combining MAPE, Control Theory, and Machine Learning](https://mlsanity.com/paper/2103.10847/) (2021)
- [Lifelong Computing](https://mlsanity.com/paper/2108.08802/) (2021)
- [Towards Quantification of Assurance for Learning-enabled Components](https://mlsanity.com/paper/2301.08980/) (2023)
- [Safety-Critical Adaptation in Self-Adaptive Systems](https://mlsanity.com/paper/2210.00095/) (2022)
- [Evaluating a Multi-Agent Voice-Enabled Smart Speaker for Care Homes: A Safety-Focused Framework](https://mlsanity.com/paper/2603.23625/) (2026)
- [Leveraging System-Level Observations to Inform Bayesian Learning of Model Parameters for Quantitative Verification](https://mlsanity.com/paper/2608.03489/) (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)
- [Trust Is Not a Score: Runtime Assurance Contracts for High-Risk AI Agents](https://mlsanity.com/paper/2609.39717/) (2026)

## Abstract (arXiv)

Building on concepts drawn from control theory, self-adaptive software handles environmental and internal uncertainties by dynamically adjusting its architecture and parameters in response to events such as workload changes and component failures. Self-adaptive software is increasingly expected to meet strict functional and non-functional requirements in applications from areas as diverse as manufacturing, healthcare and finance. To address this need, we introduce a methodology for the systematic ENgineering of TRUstworthy Self-adaptive sofTware (ENTRUST). ENTRUST uses a combination of (1) design-time and runtime modelling and verification, and (2) industry-adopted assurance processes to develop trustworthy self-adaptive software and assurance cases arguing the suitability of the software for its intended application. To evaluate the effectiveness of our methodology, we present a tool-supported instance of ENTRUST and its use to develop proof-of-concept self-adaptive software for embedded and service-based systems from the oceanic monitoring and e-finance domains, respectively. The experimental results show that ENTRUST can be used to engineer self-adaptive software systems in different application domains and to generate dynamic assurance cases for these systems.