# On the Impact of Applying Machine Learning in the Decision-Making of Self-Adaptive Systems

arXiv: https://arxiv.org/abs/2103.10194 · HTML: https://mlsanity.com/paper/2103.10194/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 44 references that are in the map.
- First posted on arXiv 18 Mar 2021.

## Builds on (most cited first, 7 of 7)

- [Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks](https://mlsanity.com/paper/1702.01135/) (2017)
- [Formal Verification of Piece-Wise Linear Feed-Forward Neural Networks](https://mlsanity.com/paper/1705.01320/) (2017)
- [Measuring Neural Net Robustness with Constraints](https://mlsanity.com/paper/1605.07262/) (2016)
- [Engineering Trustworthy Self-Adaptive Software with Dynamic Assurance Cases](https://mlsanity.com/paper/1703.06350/) (2017)
- [Perpetual Assurances for Self-Adaptive Systems](https://mlsanity.com/paper/1903.04771/) (2019)
- [Machine Learning Meets Quantitative Planning: Enabling Self-Adaptation in Autonomous Robots](https://mlsanity.com/paper/1903.03920/) (2019)
- [A Learning Approach to Enhance Assurances for Real-Time Self-Adaptive Systems](https://mlsanity.com/paper/1804.00994/) (2018)

## Built on by (most cited first, 4 of 4)

- [Lifelong Self-Adaptation: Self-Adaptation Meets Lifelong Machine Learning](https://mlsanity.com/paper/2204.01834/) (2022)
- [Deep Learning for Effective and Efficient Reduction of Large Adaptation Spaces in Self-Adaptive Systems](https://mlsanity.com/paper/2204.06254/) (2022)
- [Ensemble-based modeling abstractions for modern self-optimizing systems](https://mlsanity.com/paper/2309.05823/) (2023)
- [Hardness, Structural Knowledge, and Opportunity: An Analytical Framework for Modular Performance Modeling](https://mlsanity.com/paper/2509.11000/) (2025)

## Abstract (arXiv)

Recently, we have been witnessing an increasing use of machine learning methods in self-adaptive systems. Machine learning methods offer a variety of use cases for supporting self-adaptation, e.g., to keep runtime models up to date, reduce large adaptation spaces, or update adaptation rules. Yet, since machine learning methods apply in essence statistical methods, they may have an impact on the decisions made by a self-adaptive system. Given the wide use of formal approaches to provide guarantees for the decisions made by self-adaptive systems, it is important to investigate the impact of applying machine learning methods when such approaches are used. In this paper, we study one particular instance that combines linear regression to reduce the adaptation space of a self-adaptive system with statistical model checking to analyze the resulting adaptation options. We use computational learning theory to determine a theoretical bound on the impact of the machine learning method on the predictions made by the verifier. We illustrate and evaluate the theoretical result using a scenario of the DeltaIoT artifact. To conclude, we look at opportunities for future research in this area.