Template-Based Conjecturing for Automated Induction in Isabelle/HOL

Proof by induction plays a central role in formal verification. However, its automation remains as a formidable challenge in Computer Science. To solve inductive problems, human engineers often have to provide auxiliary lemmas manually. We automate this laborious process with template-based conjecturing, a novel approach to generate auxiliary lemmas and use them to prove final goals. Our evaluation shows that our working prototype, TBC, achieved 40 percentage point improvement of success rates for problems at intermediate difficulty level.Comment: To appear at Fundamentals of Software engineering 2023 (http://fsen.ir/2023/

LiFtEr: Language to Encode Induction Heuristics for Isabelle/HOL

Proof assistants, such as Isabelle/HOL, offer tools to facilitate inductive theorem proving. Isabelle experts know how to use these tools effectively; however, there is a little tool support for transferring this expert knowledge to a wider user audience. To address this problem, we present our domain-specific language, LiFtEr. LiFtEr allows experienced Isabelle users to encode their induction heuristics in a style independent of any problem domain. LiFtEr's interpreter mechanically checks if a given application of induction tool matches the heuristics, thus automating the knowledge transfer loop.Comment: This is the pre-print of our paper of the same title accepted at APLAS2019 (https://doi.org/10.1007/978-3-030-34175-6_14). We updated the draft after fixing the errata found by Kenji Miyamot