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Functional programs typically interact with stateful libraries that hide state behind typed abstractions. One particularly important class of applications are data structure implementations that rely on such libraries to provide a level of efficiency and scalability that may be otherwise difficult to achieve. However, because the specifications of the methods provided by these libraries are necessarily general and rarely specialized to the needs of any specific client, any required application-level invariants must often be expressed in terms of additional constraints on the (often) opaque state maintained by the library. In this paper, we consider the specification and verification of suchrepresentation invariantsusingsymbolic finite automata(SFA). We show that SFAs can be used to succinctly and precisely capture fine-grained temporal and data-dependent histories of interactions between functional clients and stateful libraries. To facilitate modular and compositional reasoning, we integrate SFAs into a refinement type system to qualify stateful computations resulting from such interactions. The particular instantiation we consider,Hoare Automata Types(HATs), allows us to both specify and automatically type-check the representation invariants of a datatype, even when its implementation depends on stateful library methods that operate over hidden state. We also develop a new bidirectional type checking algorithm that implements an efficient subtyping inclusion check over HATs, enabling their translation into a form amenable for SMT-based automated verification. We present extensive experimental results on an implementation of this algorithm that demonstrates the feasibility of type-checking complex and sophisticated HAT-specified OCaml data structure implementations layered on top of stateful library APIs.
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Data-driven abductive inference of library specifications
Programmers often leverage data structure libraries that provide useful and reusable abstractions. Modular verification of programs that make use of these libraries naturally rely on specifications that capture important properties about how the library expects these data structures to be accessed and manipulated. However, these specifications are often missing or incomplete, making it hard for clients to be confident they are using the library safely. When library source code is also unavailable, as is often the case, the challenge to infer meaningful specifications is further exacerbated. In this paper, we present a novel data-driven abductive inference mechanism that infers specifications for library methods sufficient to enable verification of the library's clients. Our technique combines a data-driven learning-based framework to postulate candidate specifications, along with SMT-provided counterexamples to refine these candidates, taking special care to prevent generating specifications that overfit to sampled tests. The resulting specifications form a minimal set of requirements on the behavior of library implementations that ensures safety of a particular client program. Our solution thus provides a new multi-abduction procedure for precise specification inference of data structure libraries guided by client-side verification tasks. Experimental results on a wide range of realistic OCaml data structure programs demonstrate the effectiveness of the approach.
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- Award ID(s):
- 1755880
- PAR ID:
- 10601887
- Publisher / Repository:
- Association for Computing Machinery (ACM)
- Date Published:
- Journal Name:
- Proceedings of the ACM on Programming Languages
- Volume:
- 5
- Issue:
- OOPSLA
- ISSN:
- 2475-1421
- Format(s):
- Medium: X Size: p. 1-29
- Size(s):
- p. 1-29
- Sponsoring Org:
- National Science Foundation
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