Accelerating Defect and Vulnerability Discovery with ML + HPC: High-Throughput Simulation Analytics for Software Quality Engineering
DOI:
https://doi.org/10.63282/3050-9246.IJETCSIT-V7I1P118Keywords:
Defect Prediction, Vulnerability Detection, High-Performance Computing, Simulation Analytics, Test Amplification, Cloud-Native Software Engineering, Explainable AI, Federated LearningAbstract
Modern software systems evolve under tight delivery cycles, heterogeneous cloud deployments, and increasingly stringent security and compliance requirements. While machine learning (ML) has demonstrated promise for predicting defect-prone and vulnerability-prone components, many deployments remain bounded by data sparsity, limited execution context, and constrained throughput of dynamic testing. Meanwhile, high-performance computing (HPC) infrastructures provide abundant parallelism, yet they are often underutilized for software quality engineering workflows that require large-scale simulation, test amplification, and telemetry-driven analytics. This manuscript proposes an integrated ML + HPC methodology for accelerating defect and vulnerability discovery through high-throughput simulation analytics. The approach treats quality discovery as a throughput-optimized, data-centric pipeline: (i) generate execution diversity via scalable simulation and test amplification, (ii) capture and normalize multi-level telemetry from builds, tests, runtime traces, and security checks, (iii) learn ranking and classification models that prioritize code regions and change sets for deeper analysis, and (iv) close the loop with root-cause triage and remediation signals. We formalize the research gap as the missing coupling between predictive models and scalable execution diversity, and we outline an evaluation framework spanning prediction quality, defect and vulnerability yield, and end-to-end cost-per-finding. The resulting methodology enables software teams to scale discovery beyond conventional CI constraints, improving early warning capability and accelerating remediation in complex enterprise-grade systems.
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