Memory-efficient fixpoint computation

Kim, Sung Kook and Venet, Arnaud J. and Thakur, Aditya V.
Formal Methods System Design (FMSD) (1) , 2025

Practical adoption of static analysis often requires trading precision for performance. This paper focuses on improving the memory efficiency of abstract interpretation without sacrificing precision or time efficiency. Computationally, abstract interpretation reduces the problem of inferring program invariants to computing a fixpoint of a set of equations. This paper presents a method to minimize the memory footprint in Bourdoncle’s iteration strategy, a widely-used technique for fixpoint computation. Our technique is agnostic to the abstract domain used. We prove that our technique is optimal (i.e., it results in minimum memory footprint) for Bourdoncle’s iteration strategy while computing the same result. We evaluate the efficacy of our technique by implementing it in a tool called MIKOS, which extends the state-of-the-art abstract interpreter IKOS. On average MIKOS demonstrated a 24.57x and 2.29x reduction in peak-memory usage compared to IKOS when verifying user-provided assertions and performing interprocedural buffer-overflow analysis, respectively.

@article{FMSD2025,
  author = {Kim, Sung Kook and Venet, Arnaud J. and Thakur, Aditya V.},
  title = {Memory-efficient fixpoint computation},
  journal = {Formal Methods System Design (FMSD)},
  volume = {65},
  number = {1},
  pages = {133--162},
  year = {2025},
  doi = {10.1007/s10703-025-00471-8},
  url = {https://doi.org/10.1007/s10703-025-00471-8},
  publisher = {Springer Nature}
}