RepoLaunch: Automating Build&Test Pipeline of Code Repositories on ANY Language and ANY Platform

The paper introduces RepoLaunch, an agent that automates the build and test pipeline for code repositories across any language and platform, thereby enabling fully automated creation of software engineering datasets for benchmarking and training coding agents.

The Gist

Imagine you have a massive library of digital recipes (code repositories) from all over the world. Some are written in French, some in Japanese, some in Python, and some in C++. Now, imagine you want to hire a robot chef to taste-test these recipes to see if they work.

The problem? Most of these recipes are broken. They are missing ingredients, the instructions are vague, the kitchen equipment is different in every country, and some recipes require a specific type of oven that you don't have. Traditionally, a human had to spend hours fixing the kitchen, buying the right ingredients, and figuring out how to turn the oven on before the robot could even taste the food.

RepoLaunch is the new "Super Kitchen Manager" robot that solves this problem.

Here is how it works, broken down into simple concepts:

1. The Problem: The "Kitchen Chaos"

In the world of software, building a project (getting code to run) is like trying to cook a complex meal in a kitchen you've never seen before.

• The Language Barrier: Some recipes use French cooking terms; others use Japanese.
• The Missing Tools: One recipe needs a blender; another needs a pressure cooker.
• The Mess: Often, the instructions are incomplete, or the ingredients are missing.

Previously, if you wanted to test a new AI chef (a Large Language Model), humans had to spend days manually fixing every single kitchen so the AI could just do the cooking. This was slow, expensive, and limited to just a few types of recipes (mostly Python on Linux).

2. The Solution: RepoLaunch (The Universal Kitchen Manager)

RepoLaunch is an AI agent designed to walk into any kitchen, regardless of the language or the country, and get it ready for cooking.

• It Speaks Every Language: Whether the code is written in C++, Java, Go, or Rust, RepoLaunch can understand the "recipe."
• It Knows Every Kitchen: It works on both Linux (the standard kitchen) and Windows (the kitchen with the different stove and tools).
• It's a Detective: It scans the messy kitchen, figures out what ingredients are missing, installs the right tools, and fixes the broken instructions.
• It's a Quality Control Inspector: Once the kitchen is ready, it runs the tests (tastes the food) to make sure the recipe actually works. If it fails, it tries again with a different approach.

3. The Magic Trick: The "Self-Healing" Recipe Book

The coolest part of RepoLaunch isn't just that it fixes the kitchen once. It learns how to fix it quickly next time.

Imagine you change one ingredient in a recipe. Usually, you'd have to re-learn the whole cooking process. RepoLaunch, however, creates a "Minimal Rebuild Command." It's like a cheat sheet that says, "Hey, you only changed the salt. Just add the salt and stir. You don't need to wash the whole kitchen again."

This allows researchers to test thousands of changes rapidly without starting from scratch every time.

4. Why This Matters: The "AI Training Gym"

The authors used RepoLaunch to build a giant gym for AI chefs.

• Before: Humans had to manually set up 100 test kitchens. It took forever.
• Now: RepoLaunch automatically sets up thousands of kitchens with different languages and operating systems.

This allows them to create a massive dataset called SWE-bench-Live. They can now throw thousands of "broken recipes" at different AI models to see which one is the best at fixing them.

5. The Results: Who Won the Cooking Contest?

They tested the best AI chefs (like Claude, GPT-4, and others) in this new gym.

• The Good News: The new generation of AI chefs is getting much better. They are solving about 30% of the problems, up from 15% before.
• The Bad News: They still struggle with the hardest languages (like C#) and the Windows kitchen, which is notoriously tricky.
• The Surprise: Even the best AI chefs sometimes get stuck in a loop, trying to fix a problem but making it worse, or they simply give up because the instructions were too confusing.

The Bottom Line

RepoLaunch is the tool that finally lets us automate the boring, difficult work of setting up software environments. It's like hiring a robot that can instantly transform a dusty, broken garage into a fully functional workshop, no matter what tools are inside.

Because of this, we can now train and test AI software engineers on a scale we've never seen before, helping us build smarter, more reliable AI that can actually help us write code in the real world.

In short: RepoLaunch is the universal translator and handyman that makes the chaotic world of software code accessible to AI, turning a "impossible mess" into a "testable playground."

Technical Summary

Here is a detailed technical summary of the paper "RepoLaunch: Automating Build&Test Pipeline of Code Repositories on ANY Language and ANY Platform."

1. Problem Statement

Building and testing software repositories is a labor-intensive process characterized by:

• High Manual Effort: Repository configurations vary widely, documentation is often incomplete, and dependency/compilation errors differ across platforms.
• Scalability Bottlenecks: Existing benchmarks (e.g., SWE-bench) and training pipelines rely heavily on manual engineering to create executable sandboxes and extract test results.
• Limitations of Current Agents: Previous attempts to automate repository setup (e.g., Repo2Run, SWE-bench-Live) were largely restricted to Python on Linux. They often failed to generalize to other languages (C++, Java, Go, etc.) or operating systems (Windows), and frequently conflated the challenges of building a repo with solving the coding task.
• The "Build" Gap: Research (GitTaskBench) indicates that 65% of agents fail to build executable environments before attempting to resolve issues, suggesting that repository setup is a distinct, critical challenge requiring dedicated automation.

2. Methodology: RepoLaunch

RepoLaunch is a multi-agent workflow designed to autonomously build, test, and manage code repositories across arbitrary programming languages and operating systems. It operates in three distinct stages:

A. Preparation Stage

• Utilities: Utilizes a library of pre-built base OS images (Docker) and language-specific instruction sets (e.g., build commands for CMake, pip, npm).
• Preparation Agent: Scans the repository file tree to identify configuration files (e.g., package.json, pom.xml, CMakeLists.txt). Based on this analysis, it selects the appropriate base image, launches a container, and copies the repository into the environment.

B. Build Stage

• Setup Agent: Uses the context from the Preparation stage to install dependencies and compile the code. It employs:
  • Bash Tool: To execute shell commands within the container.
  • WebSearch Tool: To retrieve external knowledge for resolving obscure dependency or compilation errors.
• Verify Agent: Validates the build outcome to prevent hallucinations by the Setup Agent. It inspects command history to locate test commands. If the majority of tests pass, the container is committed as a new image. If not, it reports the failure back to the Setup Agent for retry (up to a maximum limit).

C. Release Stage

• Organize Agent: Extracts reusable artifacts for long-term management:
  • Minimal Rebuild Commands: Filters the command history to create a minimal sequence of commands that can re-install dependencies and recompile the project after code modifications.
  • Test Commands & Parser: Generates a minimal set of commands to output test statuses and a Python parser to map raw logs to structured test outcomes (Pass/Fail/Skip).
  • Per-Test Commands (Optional): Generates scripts to run individual test cases for fine-grained control.
  • Dockerfile (Optional): Reconstructs a Dockerfile from the committed image layers.

D. Automated SWE Dataset Creation Pipeline

RepoLaunch enables a fully automated pipeline for creating Software Engineering (SWE) benchmarks:

1. Task Design: Humans only define the task (select repo, commit, introduce bugs, add tests).
2. Automation: RepoLaunch builds the repo, generates the Docker image, and extracts the necessary commands/parsers.
3. Validation: It applies ground-truth patches and test patches to verify that existing tests fail before the fix and pass after, ensuring the task instance is valid.

3. Key Contributions

1. Generalized Agentic Method: The first agent capable of handling any programming language (C/C++, C#, Java, Go, JS/TS, Rust, Python) and any platform (Linux, Windows) for repository build and test management.
2. Fully Automated SWE Pipeline: A system where human effort is limited to task design, while RepoLaunch handles the entire lifecycle of sandbox creation, build, and test extraction. The authors released SWE-bench-Live/MultiLang and SWE-bench-Live/Windows as demonstrations.
3. Comprehensive Failure Analysis: A detailed taxonomy of failure patterns in agentic repo building and testing, providing a roadmap for future research.

4. Experimental Results

Build and Release Success Rates

• Scope: Evaluated across 9 languages on Linux and Windows.
• Performance: RepoLaunch achieved an overall Build Success Rate of ~70% (specifically 75.63% on the evaluated subset) and a Release & Validation Success Rate of ~75.56%.
• Comparison:
  • Outperformed repo2run (Python-only, rule-based) by 18% in the Python build stage.
  • Outperformed SWE-agent (general-purpose agent) significantly in both Build and Release stages across all languages.
  • Windows Performance: While slightly lower than Linux (Build success ~64.5%), it successfully handled Windows-specific issues that previous Linux-only benchmarks missed.

Benchmarking LLMs and Agents

• New Insights: Evaluated latest LLMs (GPT-5.2, Claude-4.5, Gemini-3, DeepSeek-V3.1) on the new MultiLang and Windows datasets.
• Findings:
  • Newer LLMs show improved success rates (up to ~30% vs. ~15% for older models).
  • Claude-4.5 generally performed best.
  • Language Variance: C/C++ was the easiest; C# was the most difficult.
  • Platform Variance: Claude-4.5 remained robust on Windows, while other models saw a drop in performance.

Failure Pattern Analysis

• Build Stage Failures: The most common causes were "Repository not at production" (code not ready for build), "Build/Timeout," and "Failed dependency resolution."
• Release Stage Failures: The primary issues were "Hallucination in Build" (false positives) and "Failure to parse test logs."
• Agent Trajectory Failures: When coding agents attempted tasks on these new benchmarks, the primary failure modes were Reproduction Failure (cannot run the repo) and File/Line Localization Failure, rather than incorrect logic implementation.

5. Significance and Impact

• Scalability: RepoLaunch removes the manual bottleneck in creating executable SWE benchmarks, enabling the generation of massive, diverse datasets for training and evaluating coding agents.
• Cross-Platform Capability: By supporting Windows and multiple languages, it addresses a critical gap in current SWE research, which has been heavily skewed toward Python on Linux.
• Community Adoption: The tool is open-source and has already been adopted by other works (e.g., GLM-5 base model training) for agentic training.
• Future Research Direction: The paper highlights that the difficulty in SWE tasks often lies not in the code generation itself, but in the ability to reproduce the environment and localize the fix, suggesting future agents need better environment management and debugging capabilities.

Limitations: The system is token-intensive due to multi-turn agent interactions. It currently focuses on Linux and Windows (macOS is similar to Linux but not explicitly tested). Building large repositories requires significant CPU and I/O resources.