R-U-SURE? Uncertainty-Aware Code Suggestions By Maximizing Utility Across Random User Intents

This is the repository accompanying the paper "R-U-SURE? Uncertainty-Aware Code Suggestions By Maximizing Utility Across Random User Intents".

If you use the code released through this repository, please cite the following paper:

@article{johnson2023rusure,
title     = {{R-U-SURE?} Uncertainty-Aware Code Suggestions By Maximizing
             Utility Across Random User Intents},
author    = {Daniel D. Johnson and
             Daniel Tarlow and
             Christian Walder},
journal   = {arXiv preprint arXiv:2303.00732},
year      = {2023},
}

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Demo

If you would like to try out the R-U-SURE system, you can open our demo notebook in Google Colab:

You might also be interested in the intro and details notebooks for our utility function decision diagram representation.

Installation

If you would like to install the R-U-SURE library on your own system, you can follow the instructions below.

Prerequisite: Setting up a virtual environment

It is highly recommended to install this package into a virtual environment, as it currently depends on a patched version of numba that may be incompatible with a global installation.

To create and activate a virtual environment, you can run the Bash commands

# you can use any path here
venv_path="$HOME/venvs/rusure"
python3 -m venv $venv_path
source $venv_path/bin/activate
echo "Active virtual environment is: $VIRTUAL_ENV"

(On Linux, you may need to run sudo apt-get install python3-venv first.)

Installing the package directly from GitHub

If you want to use the r_u_sure package from Python without modifying it, you can directly install it from GitHub using pip:

# Optional: disable some unused numba features to prevent build errors
export NUMBA_DISABLE_TBB=1
export NUMBA_DISABLE_OPENMP=1

pip install "r_u_sure @ git+https://github.com/google-research/r_u_sure"

pip will then automatically install the most recent version of the package and make it available from Python via import r_u_sure.

Note that you can also add r_u_sure @ git+https://github.com/google-research/r_u_sure to your requirements.txt or pyproject.toml files if you are developing a package that depends on R-U-SURE.

Installing from source

If you prefer to download the R-U-SURE source files manually, or if you would like to contribute to the R-U-SURE library, you can perform a local installation. Start by cloning this GitHub repository:

git clone https://github.com/google-research/r_u_sure
cd r_u_sure

Next, install it:

# Optional: disable some unused numba features to prevent build errors
export NUMBA_DISABLE_TBB=1
export NUMBA_DISABLE_OPENMP=1

pip install -e .

Local edits to the source files will now be reflected properly in the python interpreter.

(If you'd prefer, you can also omit the export NUMBA_DISABLE_{X}=1 lines to compile those features into numba. Those features have additional dependencies; see the Numba documentation.)

Running tests

To run the R-U-SURE tests, you can use the command

python -m r_u_sure.testing.run_tests

Note that some tests require jit-compiling large programs, which can take a few minutes. To run a faster subset of the tests, you can instead run

python -m r_u_sure.testing.run_tests --skip_jit_tests

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This is not an officially supported Google product.