Bacardi: Fixing Breaking Dependency updates with Large Language Models

Fix breaking dependency updates

🔧 Tool Architecture Overview

Bacardi automates the repair of breaking dependency updates through a three-step pipeline:

1. Extract Failed Build Information
2. Use LLM to Generate Updated Code
3. Validate Updated Code

The following diagram and module breakdown illustrate how the components interact across the system.

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📌 Architectural Diagram

The diagram shows how build failures and API diffs flow through the system, how LLMs are invoked with prompt templates, and how fixes are validated in Docker builds.

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🗂️ Module Structure

Step 1 — Extract Failed Build Information

• git-manager:
  • Manages repositories and creates new branches for each failure category.
• extractor:
  • Extracts contextual information from the client’s repository and API diffs.
• breaking-classifier:
  • Classifies dependency-related failures and parses build logs.

Step 2 — Use LLM to Generate Updated Code

• llm:

  • Connects to LLM providers (OpenAI, Google, Anthropic, OpenRouter), resolved from .env (LLM, LLM_TYPE).

    Example

    LLM=openai
    LLM_TYPE=gpt-4o-mini

• prompts:
  • Stores reusable prompt templates tailored to different failure scenarios.

Step 3 — Validate Updated Code

• docker-build:
  • Builds reproducible Docker environments to test and validate proposed fixes.
• core:
  • Manage the full workflow, manages configurations, and outputs logs and results to results/.

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Setup and Installation

To replicate the experiments, you need to set up the environment and build the project. Follow these steps:

1. Clone the repository:

   git clone [email protected]:chains-project/bacardi.git
   cd bacardi

2. Build the project:

   mvn clean install

3. Setup environment variable:

   cp .env.example .env

Usage

Run the application using the following command:

    java -jar ./core/target/Bump.jar

To execute a single breaking dependency update:

BACARDI can target and fix one breaking dependency update at a time. Each case in the BUMP dataset is represented by a JSON record containing, among other fields, a breaking_commit hash. To repair just that one update:

1. Open the .env file.
2. Add/replace the line below, replacing <breaking_commit_hash> with the exact value from the dataset’s JSON entry:

   SPECIFIC_FILE=<breaking_commit_hash>

You can find the breaking_commit_hash in the corresponding api-diff.txt file.

3. Run the application using the following command:

    java -jar ./core/target/Bump.jar

Only the specified commit will be processed.

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LLM configuration and selection

Bacardi is designed to work flexibly with multiple Large Language Models (LLMs). It does not run on all LLMs by default.

🔧 How to Select an LLM

You can control which LLM is used via these environment variables in your .env file:

Variable	Description	Example
LLM	Name of the model to use	gpt-4o-mini, gemini-2.0-flash-001, o3-mini-2025-01-31, deepseek-deepseek-chat, qwen-qwen2.5-32b-instruct
LLM_TYPE	Type of inference provider	openai, openrouter, google,
OPENROUTER_PROVIDER (optional)	If using OpenRouter, specify the provider	open-inference/bf16, chutes/fp8
API_KEY / GOOGLE_API_KEY / OPENROUTER_API_KEY	API keys for selected providers	your-api-key-here

Bacardi currently supports these five models, each running with their respective inference providers:

• gpt-4o-mini (OpenAI)
• o3-mini-2025-01-31 (OpenAI)
• gemini-2.0-flash-001 (Google)
• deepseek-deepseek-chat (via OpenRouter)
• qwen-qwen2.5-32b-instruct (via OpenRouter)

❗ Important:

Bacardi does not automatically run on all models. You must configure the desired one before execution.

Only the configured LLM will be used when running the repair pipeline.

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Results

The results of the experiments are stored in the results directory. Each experiment is organized into subdirectories, containing the following files:

• results_<prompt_name>: Contains the results of the experiment for a specific prompt.
• <model_n>: Contains the results for a specific model.

📁 results
├── 📁 results_<prompt_name> each prompt results
│   ├── 📁 <model_n> model results
│   ├── -----
│   ├── 📁 <model_n> model results

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License

This project is licensed under the MIT License.

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Thank you for using Bacardi! If you encounter any issues or have feedback, feel free to create an issue.