Examinando por Autor "Aguirre Usandizaga, Jon"

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    Ítem
    AI-driven self-healing across the edge–cloud continuum: a systematic literature review
    (Elsevier B.V., 2026-10-01) Bonilla, Lander; Díaz de Arcaya Serrano, Josu; Aguirre Usandizaga, Jon; Almeida, Aitor
    Context: 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.
  • Miniatura
    Ítem
    ArtifactOps and ArtifactDL: a methodology and a language for conceptualizing and operationalising different types of pipelines
    (Springer Science and Business Media Deutschland GmbH, 2025-08-07) Miñón Jiménez, Raúl; Díaz de Arcaya Serrano, Josu; Torre Bastida, Ana Isabel; López de Armentia Mendizabal, Juan; Zárate Martínez, Gorka; Bonilla, Lander; Garcia Perez, Asier; Aguirre Usandizaga, Jon
    Machine learning is already integrated in diverse domains enhancing their performance and decision support. For laboratories, this approach is normally sufficient. However, in real environments, these models can not be generally deployed isolated since they require additional steps to satisfy an objective. These steps can range from different data transformations to the inclusion of extra machine learning models which compose an analytic pipeline. Moreover, the majority of software solutions wrap a model into an API and, rarely, focus on the whole pipeline. These are unresolved topics in the well-known MLOps methodology, specifically in packaging and service phases. In addition, these concerns can also be extrapolated to other paradigms like DevOps or DataOps. In the context of the Pliades European project, this paper approaches the conceptualization of diverse types of pipelines from different perspectives and for different contexts, instead of simplifying the deployment and serving to an API. Thus, ArtifactOps methodology is proposed aimed at unifying XXOps paradigms which share the majority of stages. Finally, ArtifactDL pipeline definition language is proposed to describe the key aspects identified when designing different pipelines types and to support the proposed ArtifactOps methodology. Moreover, the research presents two real scenarios to better illustrate both ArtifactOps methodology and ArtifactDL pipeline definition language and it is defined an expert evaluation conducted to validate the approach.