| | --- |
| | 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 in 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 about 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, necessary for evaluation.>, |
| | "vconfig": <dict: SMT solver flags for this definition>, |
| | "interleaved": <bool: whether this definition is interleaved from the interface file>, |
| | "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 another, 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 has the divergent effect>, |
| | "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 the 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 [commit: f3b4db2ebce90020acbbbe1b4ea0d05d3e69ad6c](https://github.com/FStarLang/FStar/commit/f3b4db2ebce90020acbbbe1b4ea0d05d3e69ad6c) |
| | from the [F* repository](https://github.com/FStarLang/FStar), using the [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* repositories on GitHib |
| | 1. [FStar](https://github.com/FStarLang/FStar): The F⋆ compiler itself, including its standard library 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} |
| | } |
| | ``` |
