Beyond Software Fault Prediction: A Multimodal Neuro-Symbolic and Causal Intelligence Framework for Proactive Defect Localization, Vulnerability Forecasting and Explainable Software Reliability Engineering
DOI:
https://doi.org/10.63282/3050-922X.IJERET-V6I1P120Keywords:
Software Defect Prediction, Vulnerability Prediction, Neuro-Symbolic AI, Causal Inference, Explainable AI, Software Reliability, Defect Localization, Aiops, Multimodal LearningAbstract
Software fault prediction has progressed from metric-based classifiers to deep representations of code changes, yet most systems remain optimized for a narrow binary question: whether a module or change is likely to be defective. That formulation is insufficient for reliability engineering because practitioners must also determine where the defect is likely to reside, whether the same change increases security exposure, which observed signals are causal rather than merely correlated, and why a recommendation should be trusted. This paper develops a research framework that unifies multimodal software evidence, neural representation learning, symbolic program knowledge, and causal reasoning into a single proactive reliability intelligence architecture. The framework ingests source-code tokens, abstract syntax and data-flow relations, change histories, issue and commit text, static-analysis findings, test outcomes, and operational telemetry. A neural encoder produces contextual risk representations; a symbolic layer enforces program, security, and engineering constraints; and a structural causal model tests candidate explanations using intervention and counterfactual reasoning. Three outputs are produced jointly: fine-grained defect localization, vulnerability forecasting, and evidence-linked explanations suitable for human review and CI/CD gating. Rather than claiming unobserved empirical gains, the paper specifies falsifiable hypotheses, mathematical objectives, ablation studies, datasets, baselines, and evaluation measures required to test the framework. The contribution is therefore an original, testable systems architecture that moves software analytics from passive probability estimation toward actionable, causally defensible reliability engineering.
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