Predictive Formal Verification: Anticipating Future Verification Requirements and Precomputing Reusable Proof Resources

The increasing complexity of modern software and hardware systems has substantially increased the computational requirements associated with formal verification. Software development frequently involves continuous code modifications, evolving system architectures, changing verification properties, and repeated validation of previously analyzed components. Techniques such as model checking, symbolic execution, satisfiability modulo theories (SMT), constraint solving, invariant generation, and deductive verification can require considerable computational resources, particularly when large state spaces or complex logical constraints are involved. Although incremental verification and proof reuse have improved the efficiency of repeated verification, existing approaches generally operate in response to an existing verification task or an already observed program modification. This paper introduces Predictive Formal Verification (PFV), a conceptual framework that investigates whether future verification requirements can be anticipated and selected verification resources can be computationally prepared before the corresponding verification tasks are formally requested. Rather than treating each verification request as an isolated computational operation, PFV analyzes historical verification activities, software development patterns, dependency relationships, code modification signals, architectural evolution, and recurring verification properties to estimate probable future verification workloads. Its architecture integrates predictive verification demand modeling, anticipated change analysis, candidate verification task generation, proactive constraint preparation, partial state-space exploration, invariant candidate preparation, reusable proof-resource management, and correctness-preserving incremental verification. A central component of the framework is a predictive proof-resource preparation mechanism that identifies computational artifacts potentially reusable across future verification tasks, including constraint representations, function summaries, proof dependencies, intermediate logical results, candidate invariants, and model-checking exploration information. The framework further introduces a strict proof-validity and compatibility mechanism to determine whether previously prepared resources remain applicable after changes to programs, specifications, models, or verification configurations. Importantly, predicted verification results are never treated as formally established correctness claims. Any reused proof resource must satisfy the applicable soundness, dependency, version, and verification conditions, while unresolved obligations must be submitted to the underlying verification engine. A unified optimization formulation is developed to evaluate the expected computational benefits of advance preparation against prediction costs, verification expenditure, storage requirements, resource contention, invalidation risks, and unused preparation. An experimental methodology is proposed to investigate whether predictive preparation can reduce total verification expenditure, accelerate subsequent verification tasks, improve development-time responsiveness, and increase computational resource utilization compared with conventional reactive verification and existing incremental verification approaches. The proposed framework does not assume that predictive verification is universally advantageous. Its effectiveness depends on the predictability of future verification requirements, the degree of computational overlap between anticipated tasks and subsequent actual tasks, the validity of reusable proof resources, and the cost of advance preparation. This research advances a predictive computational paradigm for formal methods in which verification systems attempt not only to reuse previously established computational results but also to anticipate and prepare the proof resources that future verification activities are likely to require.