Self-Adaptation in Industry: A Survey
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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.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). First posted on arXiv 6 Nov 2022.
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Citing papers per year: 2023: 1; 2024: 0; 2025: 2; 2026: 1.
4 papers.
- 2025Generative AI for Self-Adaptive Systems: State of the Art and Research Roadmapcited by 5
- 2023A User Study on Explainable Online Reinforcement Learning for Adaptive Systemscited by 3
- 2025Light over Heavy: Automated Performance Requirements Quantification with Linguistic Inducementcited by 2
- 2026Mind the Prompt: Self-adaptive Generation of Task Plan Explanations via LLMscited by 1
- 2026Mind the Prompt: Self-adaptive Generation of Task Plan Explanations via LLMs22 Apr 2026
- 2025Generative AI for Self-Adaptive Systems: State of the Art and Research Roadmap4 Dec 2025
- 2025Light over Heavy: Automated Performance Requirements Quantification with Linguistic Inducement5 Nov 2025
- 2023A User Study on Explainable Online Reinforcement Learning for Adaptive Systems9 Jul 2023