Towards Neural Synthesis for SMT-Assisted Proof-Oriented Programming

TL;DR

This paper presents a large dataset of SMT-assisted proof-oriented programs in F*, enabling AI-driven program synthesis, and demonstrates that smaller fine-tuned models can perform comparably to larger models with lower costs.

Contribution

The creation of the largest corpus of SMT-assisted F* programs and proofs, along with an analysis of AI models for program synthesis in this domain.

Findings

Smaller models like Phi-2 and StarCoder perform comparably to GPT-4.

Type-based retrieval significantly improves synthesis performance.

The dataset enables future research in AI-assisted proof-oriented programming.

Abstract

Proof-oriented programs mix computational content with proofs of program correctness. However, the human effort involved in programming and proving is still substantial, despite the use of Satisfiability Modulo Theories (SMT) solvers to automate proofs in languages such as F*. Seeking to spur research on using AI to automate the construction of proof-oriented programs, we curate a dataset of 600K lines of open-source F* programs and proofs, including software used in production systems ranging from Windows and Linux to Python and Firefox. Our dataset includes around 32K top-level F* definitions, each representing a type-directed program and proof synthesis problem producing a definition given a formal specification expressed as an F* type. We provide a program fragment checker that queries F* to check the correctness of candidate solutions. We also report on an extended version of our dataset containing a total of 940K lines of programs and proofs, with a total of 54k top-level F* definitions. We believe this is the largest corpus of SMT-assisted program proofs coupled with a reproducible program-fragment checker. Grounded in this dataset, we investigate the use of AI to synthesize programs and their proofs in F*, with promising results. Our main finding in that the performance of fine-tuned smaller language models (such as Phi-2 or StarCoder) compare favorably with large language models (such as GPT-4), at a much lower computational cost. We also identify various type-based retrieval augmentation techniques and find that they boost performance significantly. With detailed error analysis and case studies, we identify potential strengths and weaknesses of models and techniques and suggest directions for future improvements.