🐛 AssertFlip Replication Package

This is the official replication package for our paper:

AssertFlip: Reproducing Bugs via Inversion of LLM-Generated Passing Tests

AssertFlip is a system for automatically generating bug-reproducing tests from natural language reports

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🔧 Setup Instructions

1. Requirements

• Python 3.10+
• Docker
• conda (used inside Docker containers)

Install dependencies:

pip install -e .

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2. Add LLM API Credentials

The file scripts/.env is already created.

Just open it and insert your own credentials like this:

AZURE_API_KEY=your_azure_api_key
AZURE_API_BASE=https://your_azure_endpoint
AZURE_API_VERSION=2024-05-01-preview

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3. How to Run

Default (used in the paper)

python scripts/run_parallel.py

This uses:

• Agentless localization
• Pass-invert strategy
• 10 regeneration attempts
• 10 refinement attempts
• LLM validation enabled
• Planner enables

Config is controlled in scripts/config.py.

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4. Datasets

All datasets are in the datasets folder. These are the exact files used in our experiments:

• SWT_Verified_Agentless_Test_Source_Skeleton.json (default for Verified)
• SWT_Verified_Test_Source_Skeleton.json (perfect localization dataset)
• SWT_Lite_Agentless_Test_Source_Skeleton.json (default for Lite)
• SWT_Lite_Agentless_Unique_Only.json (default for Lite 188 unique instances)

To switch datasets, change:

DATASET_PATH in scripts/config.py.

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5. Running Ablations

Regeneration Ablation (0 or 5 attempts)

Edit this line in scripts/config.py:

max_regeneration_retries = 1  # for no regenerations 
# or
max_regeneration_retries = 5  # for the 5 regeneration ablation

Then run:

python scripts/run_parallel.py

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6. Running No Validation Ablation

python scripts/run_parallel_without_validation_ablation.py

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7. Running No Planner Ablation

python scripts/run_parallel_without_planner_ablation.py

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8. Perfect Localization

Change dataset in scripts/config.py to:

DATASET_PATH = "datasets/SWT_Verified_Test_Source_Skeleton.json"

Then run the default script again.

python scripts/run_parallel.py

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9. Generate Predictions

To generate preds.json from results:

python scripts/generate_preds_phases.py --results-dir results/

We also include our original prediction files in the preds_files folder for direct use.

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10. Evaluation Instructions

The previous steps produces predictions in SWT-Bench format. You can then evaluate them using SWT-Bench instructions: https://github.com/logic-star-ai/swt-bench

We also provide:

• Full outputs preds in preds_files/
• Full results after evaluating on SWT-Bench for each reported run in evaluation_results_on_SWT_Bench/

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Acknowledgment

This project uses components from the opensource test generator Coverup, licensed under the Apache 2.0 License.