# Self-Adaptation in Industry: A Survey

arXiv: https://arxiv.org/abs/2211.03116 · HTML: https://mlsanity.com/paper/2211.03116/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Nov 2022.

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

- [Generative AI for Self-Adaptive Systems: State of the Art and Research Roadmap](https://mlsanity.com/paper/2512.04680/) (2025)
- [A User Study on Explainable Online Reinforcement Learning for Adaptive Systems](https://mlsanity.com/paper/2307.04098/) (2023)
- [Light over Heavy: Automated Performance Requirements Quantification with Linguistic Inducement](https://mlsanity.com/paper/2511.03421/) (2025)
- [Mind the Prompt: Self-adaptive Generation of Task Plan Explanations via LLMs](https://mlsanity.com/paper/2604.21092/) (2026)

## Abstract (arXiv)

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 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 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 the application of self-adaptation in practice and pave the way for future industry-research collaborations.