TLDR: The paper introduces BUILD-BENCH, a new, more realistic benchmark for evaluating LLM agents on compiling diverse real-world open-source software. It also proposes OSS-BUILD-AGENT, an LLM-based agent with enhanced instruction retrieval and multi-agent error resolution, which achieves state-of-the-art performance on BUILD-BENCH by effectively handling complex compilation challenges like missing dependencies and outdated code.
Compiling open-source software (OSS) projects is a fundamental yet often challenging task in software development. It’s a process that can be labor-intensive and complex, making it an ideal test for advanced AI systems known as Large Language Model (LLM) Agents. Traditionally, automating this process has relied on predefined rules and workflows, which struggle to adapt to the unique configurations and environment setups required by diverse OSS projects.
Recent efforts to use LLMs for compilation have often focused on a limited subset of highly-rated OSS, which doesn’t fully capture the real-world difficulties. In practice, compilation instructions might be missing, dependencies undocumented, and successful builds could even require modifying source files or build scripts. This highlights a significant gap in current automated compilation methods.
Introducing BUILD-BENCH: A Realistic Benchmark for LLM Agents
To address these limitations, researchers have introduced BUILD-BENCH, a new benchmark designed to be more challenging and representative of real-world OSS compilation. Unlike previous benchmarks that focused on popular, well-maintained projects, BUILD-BENCH includes a wider variety of OSS projects, differing in quality, scale, and characteristics. This diversity ensures that evaluations on BUILD-BENCH more accurately reflect the complexities faced by software engineers.
The creation of BUILD-BENCH involved a rigorous process. Researchers collected millions of C and C++ repositories from GitHub, filtering out low-quality projects. From this vast dataset, 385 projects were randomly selected to ensure statistical representativeness. Human experts then manually attempted to build each repository, identifying 148 compilable projects for the final test set. This manual verification also included labeling ground truth for compiled binary file names and build instruction URLs, providing a robust foundation for evaluation.
A key aspect of BUILD-BENCH’s realism is its representation of project popularity and build system diversity. Most projects in BUILD-BENCH have fewer than 500 stars, reflecting the “long-tail” distribution of GitHub repositories. These less popular projects often lack extensive documentation, making them harder to compile. The benchmark also includes a variety of build systems like Make, CMake, Autotools, and Visual Studio, showcasing the heterogeneous nature of OSS development.
OSS-BUILD-AGENT: A New Baseline for Automated Compilation
Alongside BUILD-BENCH, the researchers propose OSS-BUILD-AGENT, a powerful LLM-based agent designed to tackle these compilation challenges. This system features an enhanced build instruction retrieval module and a multi-agent compilation system, enabling it to adapt to diverse OSS characteristics and achieve state-of-the-art performance.
The OSS-BUILD-AGENT operates in two main stages. First, an optional LLM-Assisted Retrieval module iteratively gathers comprehensive compilation instructions. It starts by examining the project’s README file, then identifies and explores promising links (both internal and external) to synthesize a complete set of build instructions. This process mimics how a human engineer would search for documentation, focusing on understanding the project’s setup rather than immediately diving into build scripts.
Second, a Multi-Agent Compilation System takes these instructions and iteratively generates and executes compilation steps. This system consists of two cooperating agents: a Bash Command Generator, which proposes sequences of commands, and an Execution Agent, which runs these commands in a containerized environment and provides feedback. This iterative error resolution loop allows the agent to recover from missing dependencies, incorrect flags, or environmental mismatches, a crucial capability for real-world OSS compilation.
Performance and Insights
Evaluations on BUILD-BENCH demonstrated that OSS-BUILD-AGENT significantly outperforms existing rule-based and other LLM-based compilation methods. The best configuration, using Claude 3.7-Sonnet with LLM-assisted Retrieval, achieved a 66.4% strict validated success rate, a substantial improvement over single-turn LLM baselines. This highlights the effectiveness of the agent’s iterative observation-repair-rebuild loops in resolving complex build failures.
The research also revealed that while OSS-BUILD-AGENT is model-agnostic, its performance scales with the intelligence of the underlying LLM. Stronger LLMs are more adept at adjusting their output based on error feedback and applying targeted fixes. The study also acknowledged the inherent instability of agentic frameworks, showing that performance can fluctuate across runs. However, repeated attempts (pass@k) can substantially improve success rates, suggesting a way to mitigate this non-deterministic nature.
A detailed analysis of the retrieval module showed that OSS-BUILD-AGENT’s LLM-Assisted Retrieval achieved a 73.8% accuracy in finding ground-truth build instruction URLs, significantly outperforming other agentic solutions. This success is attributed to its human-like workflow of exploring documentation rather than being distracted by noisy build scripts.
The paper also discusses common failure modes for agentic methods, including dependency resolution errors, insufficient troubleshooting, and incorrect flags. One notable success case involved OSS-BUILD-AGENT automatically patching outdated OpenCV API calls in a 10-year-old codebase, a task that rule-based approaches could not handle without human intervention. You can read the full research paper for more details here: BUILD-BENCH: Benchmarking LLM Agents on Compiling Real-World Open-Source Software.
Also Read:
- VitaBench: A New Standard for Evaluating LLM Agents in Real-World Scenarios
- SafeEvalAgent: A Dynamic Approach to AI Safety Evaluation
Future Directions
The BUILD-BENCH benchmark and the OSS-BUILD-AGENT provide valuable insights into automating OSS compilation. The researchers hope this work will inspire further innovation in agentic solutions for this complex software engineering task, ultimately benefiting software development and security communities.
