Capturing proof process

Proof automation is a common bottleneck for industrial adoption of formal meth-ods. Heuristic search techniques fail to discharge every proof obligation (PO), and significant effort is spent on proving the remaining ones interactively. Luckily, they usually fall into several proof families, where a single idea is required to dis-charge all similar POs. However, interactive formal proof requires expertise and is expensive: repeating the ideas over multiple proofs adds up to significant costs. The AI4FM research project aims to alleviate the repetitive effort by “learning” from an expert doing interactive proof. The expert’s proof attempts can give rise to reusable strategies, which capture the ideas necessary to discharge similar POs. Automatic replay of these strategies would complete the remaining proof tasks within the same family, enabling the expert to focus on novel proof ideas. This thesis presents an architecture to capture the expert’s proof ideas as a high-level proof process. Expert insight is not reflected in low-level proof scripts, therefore a generic ProofProcess framework is developed to capture high-level proof infor-