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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
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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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"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
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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,
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"vconfig": <dict:
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"interleaved": <bool: whether this definition is interleaved
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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
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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
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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 cross-project evaluation set>
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}
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```
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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)
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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
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# Data Source
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The raw data in this project are collected from eight open-source F*
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1. [FStar](https://github.com/FStarLang/FStar): The F⋆ compiler itself, including its standard
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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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# 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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"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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## 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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