Concise Read-Only Specifications for Better Synthesis of Programs with Pointers

Abstract

In program synthesis there is a well-known trade-off between concise and strong specifications: if a specification is too verbose, it might be harder to write than the program; if it is too weak, the synthesised program might not match the user's intent. In this work we explore the use of annotations for restricting memory access permissions in program synthesis, and show that they can make specifications much stronger while remaining surprisingly concise. Specifically, we enhance Synthetic Separation Logic (SSL), a framework for synthesis of heap-manipulating programs, with the logical mechanism of read-only borrows. We observe that this minimalistic and conservative SSL extension benefits the synthesis in several ways, making it more (a) expressive (stronger correctness guarantees are achieved with a modest annotation overhead), (b) effective (it produces more concise and easier-to-read programs), (c) efficient (faster synthesis), and (d) robust (synthesis efficiency is less affected by the choice of the search heuristic). We explain the intuition and provide formal treatment for read-only borrows. We substantiate the claims (a)-(d) by describing our quantitative evaluation of the borrowing-aware synthesis implementation on a series of standard benchmark specifications for various heap-manipulating programs.

Deductive Synthesis of Programs with Pointers: Techniques, Challenges, Opportunities

“…The most up-to-date implementation of SuSLik is publicly available at: https://github.com/TyGuS/suslik Table 1 collects the results of running SuSLik on benchmarks from our prior work [19,34,70,93] as well as seven new benchmarks, which we added to illustrate various challenges discussed in subsequent sections. 4 Most existing benchmarks had been adapted from the literature on verification and synthesis [24,47,50,72].…”

“…The main novelty of SuSLik was an observation that the structure of SL specifications can be used to efficiently guide the search for a program and its proof. Since then, our follow-up work has explored automatic discovery of recursive auxiliary functions [34], generating independently checkable proof certificates for synthesized programs [93], and giving the user more control over the synthesis using concise mutability annotations [19].…”