Despite the recent progress of automated program verification techniques, fully automated verification of programs manipulating recursive data structures remains a challenge. We introduce solvable tuple patterns (STPs) and conjunctive STPs (CSTPs), novel formalisms for expressing and inferring invariants between list-like recursive data structures. A distinguishing feature of STPs is that they can be efficiently inferred from only a small number of positive samples; no negative samples are required. After presenting properties and inference algorithms of STPs and CSTPs, we show how to incorporate the CSTP inference into a CHC (Constrained Horn Clauses) solver supporting list-like data structures, which serves as a uniform backend for automated program verification tools. A CHC solver incorporating the (C)STP inference has won the ADT-LIN category of CHC-COMP 2025 by a significant margin.

@article{paperbot3767,
  title = {Solvable Tuple Patterns and Their Applications to Program Verification},
  author = {Naoki Kobayashi and Ryosuke Sato and Ayumi Shinohara and Ryo Yoshinaka},
  journal = {Proceedings of the ACM on Programming Languages},
  volume = {10},
  number = {PLDI},
  year = {2026},
  doi = {10.1145/3808258}
}