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--- |
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license: cdla-permissive-2.0 |
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task_categories: |
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- text-generation |
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- text2text-generation |
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- "other" |
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tags: |
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- code |
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- fstar |
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- popai |
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pretty_name: PoPAI-FStarDataSet |
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size_categories: |
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- 10K<n<100K |
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language: |
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- code |
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- fst |
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--- |
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# Proof Oriented Programming with AI (PoPAI) - FStarDataSet |
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This dataset contains programs and proofs in [F* proof-oriented programming language](https://fstar-lang.org/). |
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The data, proposed in [Towards Neural Synthesis for SMT-Assisted Proof-Oriented Programming](https://arxiv.org/pdf/2405.01787), |
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is an archive of source code, build artifacts, and metadata assembled from eight different F⋆-based open source projects on GitHub. |
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## Primary-Objective |
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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*, |
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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). |
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## Data Format |
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Each of the examples in this dataset are organized as dictionaries with the following schema |
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```json |
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{ |
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"file_name": <str: Name of the file>, |
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"name": <str: name of the example, can be used to uniquely identify the example>, |
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"original_source_type": <str: actual source type, to be used for type checking>, |
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"source_type": <str: modified source type, to be used to formulate prompt>, |
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"source_definition": <str: target definition>, |
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"source": <dict: contains metadata about the source of this example, including project_name, git url, git sha, etc.>, |
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"source_range": <dict: metadata containing start and end lines and columns of this definition in the source file>, |
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"file_context": <str: extracted file context upto the point of current definition>, |
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"dependencies": <dict: build dependencies for this file>, |
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"opens_and_abbrevs": <list[dict]: List of opened modules and abbreviated modules in the file, necessary for evaluation.>, |
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"vconfig": <dict: SMT solver flags for this definition>, |
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"interleaved": <bool: whether this definition is interleaved from the interface file>, |
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"verbose_type": <str: the verbose type of this definition as resolved by the type checker>, |
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"effect": <str: effect>, |
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"effect_flags": <list[str]: any effect flags>, |
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"mutual_with": <list: if this definition is mutually recursive with another, list of those names>, |
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"ideal_premises": <list[str]: Other definitions that are used in the ground truth definition>, |
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"proof_features": <list[str]>, |
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"is_simple_lemma": <bool/null>, |
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"is_div": <bool: if this definition has the divergent effect>, |
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"is_proof": <bool>, |
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"is_simply_typed": <bool>, |
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"is_type": <bool/null>, |
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"partial_definition": <str>, |
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"completed_definiton": <str>, |
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"isa_cross_project_example": <bool: if this example belongs to the cross-project evaluation set> |
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} |
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``` |
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# Usage |
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To use this dataset with [`datasets`](https://pypi.org/project/datasets/), |
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```python |
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from datasets import load_dataset |
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data = load_dataset("microsoft/FStarDataSet") |
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train_data = data["train"] |
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eval_data = data["validation"] |
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test_data = data["test"] |
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intra_project_test = test_data.filter(lambda x: x["isa_cross_project_example"] == False) |
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cross_project_test = test_data.filter(lambda x: x["isa_cross_project_example"] == True) |
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``` |
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## Input |
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The primary input for generating F* definition is **`source_type`**. |
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All other information in an example may be used directly or to derive an input except |
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**`source_definition`**, **`ideal_premises`**, and **`completed_definiton`**. |
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## Output |
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The primary output is **`source_definition`**, which is the ground truth definition, that can be evaluated with the [proof checker](#evaluation-on-this-dataset). |
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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). |
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In addition, **`ideal_premises`** may be used for evaluating premise selection models. |
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# Evaluation on this dataset |
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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). |
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Download the source code and the `helpers.zip` file from the release. |
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## Troubleshooting |
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The attached binaries in the evaluator (i.e., `fstar.exe` and `z3`) are built on |
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**`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`**. |
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If any of the binaries do not work properly, build F* from [commit: f3b4db2ebce90020acbbbe1b4ea0d05d3e69ad6c](https://github.com/FStarLang/FStar/commit/f3b4db2ebce90020acbbbe1b4ea0d05d3e69ad6c) |
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from the [F* repository](https://github.com/FStarLang/FStar), using the [installation guide](https://github.com/FStarLang/FStar/blob/master/INSTALL.md). |
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# Data Source |
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The raw data in this project are collected from eight open-source F* repositories on GitHib |
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1. [FStar](https://github.com/FStarLang/FStar): The F⋆ compiler itself, including its standard library and examples. |
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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. |
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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. |
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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. |
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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. |
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6. [Steel](https://github.com/FStarLang/steel): A concurrent separation logic library, with proofs of data structures and concurrency primitives. |
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7. [miTLS-F*](https://github.com/project-everest/mitls-fstar): A partially verified reference implementation of the TLS protocol. |
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8. [EverQuic-Crypto](https://github.com/project-everest/everquic-crypto): A verified implementation of header and packet protection for the QUIC protocol. |
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# Limitations |
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**TDB** |
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# Citation |
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``` |
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@inproceedings{chakraborty2024towards, |
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title={Towards Neural Synthesis for SMT-Assisted Proof-Oriented Programming}, |
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author={Chakraborty, Saikat and Ebner, Gabriel and Bhat, Siddharth and Fakhoury, Sarah and Fatima, Sakina and Lahiri, Shuvendu and Swamy, Nikhil}, |
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booktitle={Proceedings of the IEEE/ACM 47th International Conference on Software Engineering (To Appear)}, |
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pages={1--12}, |
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year={2025} |
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} |
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``` |
