LLMs now find compiler bugs that traditionally needed 40,000 lines of handwritten code to discover
The paper introduces a novel method using LLMs to detect compiler optimization bugs through iterative code mutation, requiring only 150 lines of code to implement versus traditional approaches needing 40,000+ lines.
https://arxiv.org/abs/2501.00655
Original Problem 🤔:
→ Testing compilers for missed optimizations is complex, requiring extensive handwritten code generators (40,000+ lines)
→ Traditional approaches focus mainly on functional correctness rather than finding optimization opportunities
→ Building test generators for new languages is prohibitively expensive
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Solution in this Paper 💡:
→ Uses LLMs to mutate simple seed programs through predetermined instructions
→ Implements four differential testing strategies to detect optimization bugs
→ Validates results using sanitizers and dynamic analysis
→ Requires only 150 lines of Python code to implement
→ Can be easily adapted for multiple programming languages
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Key Insights 💭:
→ LLMs can effectively generate complex test cases without grammar-based generators
→ Simple seed programs can evolve into sophisticated test cases through guided mutation
→ Differential testing strategies can reliably identify optimization opportunities
→ The approach scales across languages with minimal modification
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Results 📊:
→ Found 24 confirmed bugs in production compilers
→ 96.45% of generated code successfully compiles
→ Works across C/C++, Rust, and Swift
→ Required only one week of compute time
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