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Agentic loops
Dan Luu argues that high-quality AI-driven software development requires a shift away from human code review toward automated, randomized testing and fuzzing.
What: Drawing on his experience at Centaur, where chip design relied on massive randomized test suites instead of manual review, Luu advocates for using LLMs to drive fuzzers rather than just generating PRs. He notes that LLMs are currently poor at writing tests but highly effective at augmenting fuzzing loops if directed by humans.
Why it matters: As AI productivity accelerates code generation, human review bottlenecks will lead to increased bug rates; adopting hardware-style verification is a more scalable alternative.
Deep dive
- Manual code review is increasingly ineffective for AI-generated code volumes; automated testing is required to maintain quality.
- Randomized testing and fuzzing are more efficient than unit tests for complex system verification.
- LLMs are bad at writing 'human-style' unit tests but can be steered to write highly effective fuzzers.
- False positive rates in AI-driven bug reporting can be reduced by using 'contrarian' agent personas and independent verification loops.
- Benchmarks for models are often noisy and misleading; internal, task-specific evaluation is more valuable than summary scores.
Decoder
- Fuzzing: The process of providing random, malformed, or unexpected inputs to a computer program to trigger crashes or find security vulnerabilities.
- Property-based testing: A testing methodology where you define general invariants (the 'properties') that the code must always satisfy, and the computer generates thousands of test cases to try and break them.
- SWE-Bench: A popular benchmark used to evaluate how well LLMs can resolve real-world software issues from GitHub repositories.
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