Archiki Prasad, Elias Stengel-Eskin, Justin Chih-Yao Chen, Zaid Khan, Mohit Bansal

• 🤗 Debugging Datasets

This repository contains the code for our paper Learning to Generate Unit Tests for Automated Debugging. We present UTGen, a data curation and training method for teaching models to generate unit tests (inputs and outputs for a given function), and UTDebug, a debugging pipeline that uses generated unit tests for automated code-debugging with LLMs. In this repo, we provide the code for UTDebug to evaluate unit tests extrinsically and a script to evaluate attack rate, output accuracy, and acc $\cap$ attack on three debugging datasets: HE+Fix, MBPP+Fix, and MBPP+Fix (Hard).

This project is built on Python 3.10.11. All dependencies can be installed via:

pip install -r requirements.txt

• Run scripts he_intrinsic_eval.py for HE+Fix and mbpp_intrinsic_eval.py for MBPP+Fix and MBPP+Fix Hard. For the latter add the --hard argument. When evaluating off-the-shelf models, please pass the argument --eval-base, or otherwise provide a trained LoRA checkpoint dir via --ckpt-dir. For randomly-sampled UTs use the argument --random.

Example for evaluating trained Qwen2.5 on MBPP+Hard as done in the paper:

python mbpp_intrinsic_eval.py --model qwen --ckpt-dir <path to ckpt> --use-temp --num-units 3

Example for evaluating randomly-sampled UTs from Qwen2.5 on MBPP+Hard as done in the paper:

python mbpp_intrinsic_eval.py --model qwen --eval-base --random --use-temp --num-units 3

The UT generation method is provided using the --unit-mode argument. Use --unit-mode joint_sc for the prompted failing UTs, --unit-mode train_joint_sc for UTGen or any trained models, and --unit-mode random_joint_sc for randomly-sampled UTs. Note that by default, round 0 evaluated the given erroneous code in the dataset, so set --max-turns <number of debug rounds> + 1

Example for running UTDebug with UTGen trained model on MBPP+Hard as done in the paper with 3 UTs and 3 turns:

python mbpp_utdebug.py --model qwen --ckpt-dir <path to ckpt> --units 3 --max-turns 4 --backtrack --unit-mode train_joint_sc --dataset mbpp_plus_fix_hard

The no UT feedback baseline is implemented in no_ut_{he/mbpp}.py files and only takes --dataset, --model, and --max-turns arguments.

Please cite our paper as

@article{prasad2025unit,
    title = {Learning to Generate Unit Tests for Automated Debugging},
    author = {Prasad, Archiki and Stengel-Eskin, Elias and Chen, Justin Chih-Yao and Khan, Zaid and Bansal, Mohit}, 
    year = {2025},
    journal={arXiv preprint 2502.01619} 
}