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

Source: arXiv - 2603.05026v1

Overview

RepoLaunch is a groundbreaking AI‑driven agent that can automatically resolve dependencies, compile source code, and run tests for any GitHub‑hosted repository—regardless of the programming language or operating system. By turning the traditionally manual “build‑and‑test” step into a fully automated service, the authors open the door to massive, low‑cost generation of software‑engineering datasets and scalable benchmarking of coding agents.

Key Contributions

• Universal Build & Test Agent – First LLM‑based system that works across any language (e.g., Python, Rust, Java, Go, Haskell) and any OS (Linux, macOS, Windows).
• End‑to‑End Automation Pipeline – A self‑contained workflow that starts from a raw repository URL and ends with a structured test‑result report, requiring only a high‑level task description from a human.
• Dataset‑Creation Engine – Demonstrates how RepoLaunch can automatically generate large‑scale SWE (Software Engineering) datasets, eliminating the manual labor that has bottlenecked prior research.
• Open‑Source Reference Implementation – The authors release the agent code, prompts, and a benchmark suite, enabling immediate reuse by the community.
• Adoption in Emerging Benchmarks – Several recent papers on agentic benchmarking and LLM training already integrate RepoLaunch for automated task generation, proving its practical impact.

Methodology

1. Repository Ingestion – RepoLaunch accepts a Git URL, clones the repo, and inspects its file tree to infer the primary language(s) and build system (e.g., setup.py, Cargo.toml, Makefile).
2. LLM‑Powered Dependency Resolution – A large language model (GPT‑4‑style) is prompted with the detected build configuration and asked to generate the exact shell commands needed to install system‑level packages, language‑specific libraries, and any custom scripts.
3. Dynamic Environment Provisioning – Using lightweight containers (Docker for Linux/macOS, Windows containers for Windows), RepoLaunch spins up an isolated environment matching the target OS.
4. Build Execution & Monitoring – The generated commands are executed step‑by‑step. The agent watches stdout/stderr, detects failures, and iteratively refines the commands (e.g., adding missing apt-get packages) until the build succeeds or a timeout is reached.
5. Test Discovery & Running – Once compiled, RepoLaunch automatically discovers test suites (e.g., pytest, cargo test, npm test) and runs them, capturing pass/fail outcomes, coverage metrics, and any runtime errors.
6. Result Normalization – All outputs are converted into a uniform JSON schema (repository ID, build status, test results, logs) that downstream tools can ingest for benchmarking or dataset creation.

The whole loop is orchestrated by a lightweight controller script; the “brain” of the system is the LLM agent that translates ambiguous build instructions into concrete, reproducible commands.

Results & Findings

Key takeaways

• Language‑agnostic success – Even for languages with notoriously tricky native toolchains (e.g., Rust + OpenSSL), the LLM was able to infer the right system packages most of the time.
• Rapid iteration – The average end‑to‑end time per repo was under 5 minutes, making large‑scale dataset generation feasible on modest cloud resources.
• Error‑recovery loop – The agent’s ability to “ask itself” for missing dependencies reduced manual debugging cycles dramatically compared to a naïve static script.

The authors also report that the generated datasets (≈ 1.2 M build‑test pairs) have already been used to train and evaluate several next‑generation coding agents, yielding measurable improvements in downstream code‑generation benchmarks.

Practical Implications

In short, RepoLaunch turns a painful, manual step into a reusable service, enabling faster onboarding, more reliable CI, and richer data for AI‑driven software engineering.

Limitations & Future Work

• Complex Native Toolchains – Projects that require custom kernel modules, GPU drivers, or proprietary binaries still cause failures; the LLM’s knowledge base may miss obscure system packages.
• Security & Sandboxing – Running arbitrary build scripts poses a risk; the current implementation relies on container isolation but does not perform deep static analysis of potentially malicious commands.
• Scalability on Large Monorepos – While fast on typical open‑source repos, very large monorepos (hundreds of millions of lines) exceed the current timeout thresholds.
• Prompt Sensitivity – The quality of generated commands can vary with the LLM version; future work includes fine‑tuning a domain‑specific model to reduce variability.

The authors outline several next steps: tighter integration with platform‑specific package managers (e.g., conda, brew), support for orchestrated multi‑container builds (Kubernetes), richer error‑explanation modules, and a public “RepoLaunch as a Service” offering for on‑demand builds.

RepoLaunch demonstrates that with the right blend of LLM reasoning and containerized execution, the once‑tedious “build‑and‑test” phase can become a plug‑and‑play component of modern software engineering workflows.

Authors

• Kenan Li
• Rongzhi Li
• Linghao Zhang
• Qirui Jin
• Liao Zhu
• Xiaosong Huang
• Geng Zhang
• Yikai Zhang
• Shilin He
• Chengxing Xie
• Xin Zhang
• Zijian Jin
• Bowen Li
• Chaoyun Zhang
• Yu Kang
• Yufan Huang
• Elsie Nallipogu
• Saravan Rajmohan
• Qingwei Lin
• Dongmei Zhang

Paper Information

• arXiv ID: 2603.05026v1
• Categories: cs.SE, cs.LG, cs.MA
• Published: March 5, 2026
• PDF: Download PDF