Datasets:

microsoft
/

FStarDataSet-V2

---
license: cdla-permissive-2.0
task_categories:
- text-generation
- text2text-generation
- other
tags:
- code
- fstar
- popai
pretty_name: PoPAI-FStarDataSet-V2
size_categories:
- 10K<n<100K
language:
- code
- fst
---

This dataset is the Version 2.0 of [`microsoft/FStarDataSet`](https://huggingface.co/datasets/microsoft/FStarDataSet).


## 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-v2.0](https://github.com/FStarLang/fstar_dataset/releases/tag/eval-v2.0). 
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 (10183ea187da8e8c426b799df6c825e24c0767d3)](https://github.com/FStarLang/FStar/commit/10183ea187da8e8c426b799df6c825e24c0767d3) 
from the [F* repository](https://github.com/FStarLang/FStar), using their [installation guide](https://github.com/FStarLang/FStar/blob/master/INSTALL.md).

# Data Source
In addition to the eight projects in `microsoft/FStarDataSet`, data from four more projects are included in this version. 
1. [Starmada](https://github.com/microsoft/Armada):  a framework for doing proofs by stepwise refinement for concurrent programs in a weak memory model. Starmada is an experimental version of Armada implemented in F⋆, relying on various advanced features of F⋆’s dependent type system for more generic and abstract proofs.
2. [Zeta](https://github.com/project-everest/zeta): a high performance, concurrent monitor for stateful services proven correct in F⋆ and its Steel concurrent separation logic
3. [Dice-star](https://github.com/verified-HRoT/dice-star):  a verified implementation of the DICE measured boot protocol for embedded devices
4. [Noise-star](https://github.com/Inria-Prosecco/noise-star): a verified compiler for implementations of Noise protocols, a family of key-exchange protocols

# 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}
}
```