Bonilla, LanderDíaz de Arcaya Serrano, JosuAguirre Usandizaga, JonAlmeida, Aitor2026-08-132026-08-132026-10-01Bonilla, L., Diaz-de-Arcaya, J., Aguirre-Usandizaga, J., & Almeida, A. (2026). AI-driven self-healing across the edge–cloud continuum: a systematic literature review . Information and Software Technology, 198. Elsevier B.V. https://doi.org/10.1016/J.INFSOF.2026.1082320950-584910.1016/J.INFSOF.2026.108232https://hdl.handle.net/20.500.14454/6499Context: The increasing complexity posed by Edge–Cloud Continuum architectures requires innovative software development practices and paradigms. Traditional software engineering practices fall short for addressing the particularities of such deployments; hence, there is a critical need for innovative tools and techniques capable of autonomous self-remediation and enhanced resilience with minimal human intervention. Objective: The overarching goal of this study is to synthesize the state-of-the-art with regard to AI-driven self-healing in the computing continuum. Implementation challenges and ethical implications are analyzed to identify effective approaches, architectures, and future trends in the field of automated program repair. Methods: A Systematic Literature Review was conducted following the PRISMA 2020 guidelines and Kitchenham’s methodology for software engineering. A rigorous automated search across designated academic databases initially yielded 2930 records. After applying strict inclusion and exclusion criteria, 99 primary studies were selected. Results: The analysis reveals a fundamental paradigm shift from observational monitoring to actionable, autonomous repair. While LLM-driven approaches and Agentic frameworks demonstrate high efficacy in tasks such as causal discovery and complex code patching, a critical validation gap remains regarding their deployment in non-deterministic runtime environments. Current literature often lacks the rigorous testing frameworks required to ensure that AI-generated repairs do not introduce regressions or security vulnerabilities. Conclusion: Achieving trustworthy autonomy requires a holistic approach that merges advanced AI paradigms with disciplined software engineering. Future research must prioritize the creation of standardized benchmarks and empirical studies to bridge the gap between theoretical algorithms and industrial application. Furthermore, the development of decentralized multi-agent architectures and Human-in-the-Loop techniques is essential to ensure safety, explainability, and ethical compliance in the next generation of resilient software systems.eng© 2026 The AuthorsAgentic AIAI agentAIOpsEdge–cloud continuumEthicsSelf-healingSystematic literature reviewreview article2026-08-13