LLVM-Bench: Benchmarking and Advancing Large Language Models for LLVM Compiler Issue Resolution
arXiv:2607.00700v1 Announce Type: cross Abstract: LLVM is a widely used compiler infrastructure whose scale and complexity make issue resolution labor-intensive and challenging. Although large language models (LLMs) have recently achieved remarkable success in issue resolution, their effectiveness on complex system-level LLVM compiler remains largely unexplored. To address this gap, we introduce LLVM-Bench, the first large-scale benchmark for LLVM issue resolution, containing 423 real-world, validated tasks collected from the LLVM project. We further develop LLVM-Gym, a scalable evaluation pla
The proliferation of increasingly capable large language models is driving exploration into their application in complex, specialized engineering domains like compiler development.
This development indicates a tangible path for AI to significantly reduce the labor and expertise required for maintaining intricate system-level software infrastructure, improving efficiency and innovation cycles.
The potential burden of compiler issue resolution may be substantially lightened through AI assistance, enabling faster development, debugging, and optimization of critical software components.
- · Software Developers
- · Cloud Computing Providers
- · Hardware Manufacturers
- · AI Tooling Companies
- · Manual Debugging Services
- · Firms reliant solely on human-intensive compiler maintenance
Large language models will become increasingly integrated into compiler development workflows, automating parts of the debugging and resolution processes.
The efficiency gains in compiler development could accelerate the pace of innovation in programming languages, operating systems, and specialized hardware architectures.
Reduced compiler complexity barriers could enable a wider range of developers and organizations to contribute to and customize fundamental software infrastructure, potentially democratizing system-level programming.
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Read at arXiv cs.AI