Background: Rapid adoption of cloud services and heterogeneous hardware accelerators—especially graphics processing units (GPUs)—has transformed both production and testing landscapes. Modern test infrastructures must reconcile software evolution, fault tolerance, and cloud-native paradigms to deliver reliable, scalable verification for high-volume GPU manufacturing and serverless applications. This article synthesizes established theory in software evolution and fault tolerance with contemporary cloud platform characteristics and model-based testing techniques to propose a comprehensive, textually detailed design for fault-tolerant test infrastructures.

Objective: To present an integrated, publication-quality architectural and methodological treatment for designing fault-tolerant, cloud-enabled test infrastructures that serve large-scale GPU manufacturing lines and serverless application testing, grounded strictly in the provided literature.

Methods: We develop a conceptual architecture and method suite that combines principles of software evolution and maintainability (Lehman & Ramil, 2002; Chapin et al., 2001), classical software fault tolerance (Somani & Vaidya, 1997; Torres-Pomales, 2000), and cloud characteristics (Wilkins, 2019; Patterson, 2019; Saraswat & Tripathi, 2020) with rigorous model- and graph-transformation–based test generation approaches (van der Aalst et al., 2004; Baldan et al., 2004; Baresi et al., 2006). We derive operational patterns for serverless orchestration, resilience engineering and fault injection, and integrate specification matching and counterexample-based test generation techniques (Beyer et al., 2004; Cherchago & Heckel, 2004). The design is validated by a descriptive results section that interprets how the architecture addresses typical failure modes and operational constraints in GPU manufacturing and cloud testbeds, and by a discussion of limitations and future research directions.

Results: The architecture organizes layered fault containment, adaptive test scheduling, and cloud-native resource management to achieve graceful degradation, high observability, and maintainability in the face of hardware failures, transient cloud faults, and evolving software test artifacts. When mapped to best-practice cloud features—serverless functions, event-driven pipelines, and managed infrastructure—this design offers predictable scalability and cost containment while preserving rigorous test coverage and traceability.

Conclusion: A cohesive synthesis of fault-tolerance theory, software evolution principles, and cloud-specific operational mechanics yields a practical, extensible blueprint for fault-tolerant test infrastructures suitable for large-scale GPU manufacturing and serverless testing. The proposed blueprint clarifies trade-offs, prescribes concrete resilience patterns, and identifies research avenues for empirical evaluation and automation.

Fault tolerance, cloud-native testing, GPU manufacturing, serverless

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