metadata

license: cdla-permissive-2.0
task_categories:
  - text-generation
  - text2text-generation
  - other
tags:
  - code
  - fstar
  - popai
pretty_name: PoPAI-FStarDataSet
size_categories:
  - 10K<n<100K
language:
  - code
  - fst

Proof Oriented Programming with AI (PoPAI) - FStarDataSet

This dataset contains programs and proofs in F* proof-oriented programming language. The data, proposed in Towards Neural Synthesis for SMT-Assisted Proof-Oriented Programming. is an archive of source code, build artifacts, and metadata assembled from eight different F⋆-based open source projects on GitHub.

Primary-Objective

This dataset's primary objective is to train and evaluate Proof-oriented Programming with AI (PoPAI, in short). Given a specification of a program and proof is F*, the objective of a AI model is to synthesize the implemantation (see below for details about the usage of this dataset, including the input and output).

Data Format

Each of the examples in this dataset are organized as dictionaries with the following schema

{
    "file_name": <str: Name of the file>,
    "name": <str: name of the example, can be used to uniquely identify the example>,
    "original_source_type": <str: actual source type, to be used for type checking>,
    "source_type": <str: modified source type, to be used to formulate prompt>,
    "source_definition": <str: target definition>,
    "source": <dict: contains metadata acout the source of this example, including project_name, git url, git sha, etc.>,
    "source_range": <dict: metadata containing start and end lines and columns of this definition in the source file>,
    "file_context": <str: extracted file context upto the point of current definition>, 
    "dependencies": <dict: build dependencies for this file>,
    "opens_and_abbrevs": <list[dict]: List of opened modules and abbreviated modules in the file, necessry for evaluating.>,
    "vconfig": <dict: variour buils configuration for this definition>,
    "interleaved": <bool: whether this definition is interleaved with another>,
    "verbose_type": <str: the verbose type of this definition as resolved by the type checker>,
    "effect": <str: effect>,
    "effect_flags": <list[str]: any effect flags>,
    "mutual_with": <list: if this definition is mutually recursive with other, list of those names>,
    "ideal_premises": <list[str]: Other definitions that are used in the ground truth definition>,
    "proof_features": <list[str]>,
    "is_simple_lemma": <bool/null>,
    "is_div": <bool: if this definition is divergent>,
    "is_proof": <bool>,
    "is_simply_typed": <bool>,
    "is_type": <bool/null>,
    "partial_definition": <str>,
    "completed_definiton": <str>,
    "isa_cross_project_example": <bool: if this example belongs to cross-project evaluation set>
}

Usage

To use this dataset with datasets,

from datasets import load_dataset

data = load_dataset("microsoft/FStarDataSet")
train_data = data["train"]
eval_data = data["validation"]
test_data = data["test"]

intra_project_test = test_data.filter(lambda x: x["isa_cross_project_example"] == False)
cross_project_test = test_data.filter(lambda x: x["isa_cross_project_example"] == True)

Input

The primary input for generating F* definition is source_type. All other information in an example may be used directly or to derive an input except source_definition, ideal_premises, and completed_definiton.

Output

The primary output is source_definition, which is the ground truth definition, that can be evaluated with the proof checker. The completed_definiton may be used as ground truth when a model is used as a text completion setting (though the evaluator does not support evaluation in this setting). In addition, ideal_premises may be used for evaluating premise selection models.

Evaluation on this dataset

Generated F* definitions should be evaluated the proof checker tool from https://github.com/FStarLang/fstar_dataset/releases/tag/eval-v1.0. Download the source code and the helpers.zip file from the release.

Troubleshooting

The attached binaries in the evaluator (i.e., fstar.exe and z3) are built on Ubuntu 20.04.6 LTS (GNU/Linux 5.4.0-189-generic x86_64) gcc (Ubuntu 9.4.0-1ubuntu1~20.04.2), OCaml 4.12.0. If any of the binaries do not work properly, build F* from this commit (f3b4db2ebce90020acbbbe1b4ea0d05d3e69ad6c) from the F* repository, using their installation guide.

Data Source

The raw data in this project are collected from eight open-source F* reposiroties in GitHib

FStar: The F⋆ compiler itself, including its standard libraries and examples.
Karamel: A transpiler from a subset of F⋆ called Low* to C, including libraries to work with a model of C types and control structures, e.g., for- and while-loops.
EverParse: A parser generator for binary formats, used in various large scale systems, e.g., the Windows kernel.
HACL*: A library of verified cryptographic algorithms, including ValeCrypt, a library of verified assembly code, as well as EverCrypt, a cryptographic provider, including code deployed in Linux, Firefox, and Python.
Merkle-tree: A verified, incremental Merkle tree, designed for use in Azure CCF, a confidential computing system.
Steel: A concurrent separation logic library, with proofs of data structures and concurrency primitives.
miTLS-F*: A partially verified reference implementation of the TLS protocol.
EverQuic-Crypto: A verified implementation of header and packet protection for the QUIC protocol.

Limitations

TDB

Citation

@inproceedings{chakraborty2024towards,
  title={Towards Neural Synthesis for SMT-Assisted Proof-Oriented Programming},
  author={Chakraborty, Saikat and Ebner, Gabriel and Bhat, Siddharth and Fakhoury, Sarah and Fatima, Sakina and Lahiri, Shuvendu and Swamy, Nikhil},
  booktitle={Proceedings of the IEEE/ACM 47th International Conference on Software Engineering (To Appear)},
  pages={1--12},
  year={2025}
}

Datasets:

microsoft
/

FStarDataSet