Proving the Coding Interview: A Benchmark for Formally Verified Code Generation

We introduce the Formally Verified Automated Programming Progress Standards, or FVAPPS, a benchmark of 4715 samples for writing programs and proving their correctness, the largest formal verification benchmark, including 1083 curated and quality controlled samples. Previously, APPS provided a benchmark and dataset for programming puzzles to be completed in Python and checked against unit tests, of the kind seen in technical assessments in the software engineering industry. Building upon recent approaches for benchmarks in interactive theorem proving, we generalize the unit tests to Lean 4 theorems given without proof (i.e., using Lean’s “sorry” keyword). On the 406 theorems of 100 randomly selected samples, Sonnet correctly proves 30% and Gemini correctly proves 18%. We challenge the machine learning and program synthesis communities to solve both each general purpose programming problem and its associated correctness specifications. The benchmark is available at https://huggingface.co/datasets/quinn-dougherty/fvapps.

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Publication Details

Published
2025-05-03
DOI
https://doi.org/10.1109/llm4code66737.2025.00014
Citations
3
Primary Topic
Software Testing and Debugging Techniques
Type
article
Field-Weighted Citation Impact
6.92
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article

Proving the Coding Interview: A Benchmark for Formally Verified Code Generation

Ronak Mehta, Quinn Dougherty
3 citations
Software Testing and Debugging Techniques
6.92
article

Proving the Coding Interview: A Benchmark for Formally Verified Code Generation

Ronak Mehta, Quinn Dougherty
article en
3 citations

Abstract

We introduce the Formally Verified Automated Programming Progress Standards, or FVAPPS, a benchmark of 4715 samples for writing programs and proving their correctness, the largest formal verification benchmark, including 1083 curated and quality controlled samples. Previously, APPS provided a benchmark and dataset for programming puzzles to be completed in Python and checked against unit tests, of the kind seen in technical assessments in the software engineering industry. Building upon recent approaches for benchmarks in interactive theorem proving, we generalize the unit tests to Lean 4 theorems given without proof (i.e., using Lean’s “sorry” keyword). On the 406 theorems of 100 randomly selected samples, Sonnet correctly proves 30% and Gemini correctly proves 18%. We challenge the machine learning and program synthesis communities to solve both each general purpose programming problem and its associated correctness specifications. The benchmark is available at https://huggingface.co/datasets/quinn-dougherty/fvapps.

Beneficial Designs (United States) (US)
Openalex Percentile: Top 4%
Software Testing and Debugging Techniques
6.92
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