Given the high cost of formal verification, a large system may include differently analyzed components: a few are fully verified, and the rest are tested. Currently, there is no reasoning system that can soundly compose these heterogeneous analyses and derive the overall formal guarantees of the entire system. The traditional compositional reasoning technique—rely-guarantee reasoning—is effective for verified components, which undergo over-approximated reasoning, but not for those components that undergo under-approximated reasoning, e.g., using testing or other program analysis techniques. The goal of this paper is to develop a formal, logical foundation for composing heterogeneous analysis, deploying both over-approximated (verification) and under-approximated (testing) reasoning. We focus on systems that can be modeled as a collection of communicating processes. Each process owns its internal resources and a set of channels through which it communicates with other processes. The key idea is to quantify the guarantees obtained about the behavior of a process as a test level , which captures the constraints under which this guarantee is analyzed to be true. We design a novel proof system LabelBI based on the logic of bunched implications that enables rely-guarantee reasoning principles for a system of differently analyzed components. We develop trace semantics for this logic, against which we prove our logic is sound. We also prove cut elimination of our sequent calculus. We demonstrate the expressiveness of our logic via a case study.

@article{paperbot2971,
  title = {Quantified Underapproximation via Labeled Bunches},
  author = {Lang Liu and Farzaneh Derakhshan and Limin Jia and Gabriel A. Moreno and Mark Klein},
  journal = {Proceedings of the ACM on Programming Languages},
  volume = {9},
  number = {OOPSLA2},
  year = {2025},
  doi = {10.1145/3763051}
}