|
--- |
|
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](https://fstar-lang.org/). |
|
The data, proposed in [Towards Neural Synthesis for SMT-Assisted Proof-Oriented Programming](https://arxiv.org/pdf/2405.01787). |
|
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](#usage) 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 |
|
```json |
|
{ |
|
"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`](https://pypi.org/project/datasets/), |
|
```python |
|
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](#evaluation-on-this-dataset). |
|
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](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)](https://github.com/FStarLang/FStar/commit/f3b4db2ebce90020acbbbe1b4ea0d05d3e69ad6c) |
|
from the [F* repository](https://github.com/FStarLang/FStar), using their [installation guide](https://github.com/FStarLang/FStar/blob/master/INSTALL.md). |
|
|
|
|
|
# Data Source |
|
The raw data in this project are collected from eight open-source F* reposiroties in GitHib |
|
1. [FStar](https://github.com/FStarLang/FStar): The F⋆ compiler itself, including its standard libraries and examples. |
|
2. [Karamel](https://github.com/FStarLang/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. |
|
3. [EverParse](https://github.com/project-everest/everparse): A parser generator for binary formats, used in various large scale systems, e.g., the Windows kernel. |
|
4. [HACL*](https://github.com/hacl-star/hacl-star): 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. |
|
5. [Merkle-tree](https://github.com/hacl-star/merkle-tree): A verified, incremental Merkle tree, designed for use in Azure CCF, a confidential computing system. |
|
6. [Steel](https://github.com/FStarLang/steel): A concurrent separation logic library, with proofs of data structures and concurrency primitives. |
|
7. [miTLS-F*](https://github.com/project-everest/mitls-fstar): A partially verified reference implementation of the TLS protocol. |
|
8. [EverQuic-Crypto](https://github.com/project-everest/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} |
|
} |
|
``` |
|
|
|
# Contributors |
|
1. [Saikat Chakraborty](https://huggingface.co/saikatc) |
|
2. [Gabriel Ebner]() |
|
3. [Nikhil Swamy]() |
|
4. [Siddharth Bhat]() |
