Engineering Self-Adaptive and Autonomous Systems: A Critical Survey and an Adaptation–Assurance Framework

Abstract

: Self-adaptive systems (SAS) autonomously reconfigure in response to uncertain environments and evolving requirements. Two decades of research have produced a rich but fragmented body of work spanning architecture, machine learning, uncertainty management, and verification, each strand typically surveyed in isolation. This paper makes three contributions. First, it provides a critical survey, rather than a catalogue, of foundational and contemporary SAS methodologies, organized using the reporting principles of established systematic-review guidelines. Second, it introduces an original synthesizing artifact, the Adaptation–Assurance Gap Map, which positions each class of adaptation mechanism (rule-based, control-theoretic, search-based, model-based, reinforcement learning, deep predictive, federated, and LLM-assisted) against the maturity of assurance evidence routinely available for it, exposing systematically under-assured combinations. The map is governed by an explicit rating rubric, complemented by a technique-maturity model that includes an assurance-cost dimen-sion. Third, it positions the framework against prior SAS taxonomies and converts the findings into a gap-to-research traceability matrix with concrete, falsifiable directions. Using exemplars (DeltaIoT, SWIM, and an ML-component retraining case), we show that the field’s principal weakness is not a shortage of adaptation mechanisms but a shortage of assurance that scales with them in scope, tempo, and cost.