● Covered by 1 source · 1 reportLow impact1 neutral
Exploring Fuzzing Techniques for Gleam Compiler Bug Detection
Key points
- Developer investigated bug detection methods for the Gleam compiler.
- LLM-based fuzzing found one bug but was expensive and non-deterministic.
- Structure-aware fuzzing is being considered as an alternative.
- Fuzzing generates random inputs to uncover edge cases in software.
Introduction to Compiler Bug Detection
A developer has been monitoring the Gleam compiler's changelog and issue tracker, noting recurring issues related to code generation and output discrepancies between Erlang and JavaScript. This observation prompted an investigation into automated methods for uncovering compiler bugs.
LLM-Based Fuzzing Attempt
The initial approach involved using a Large Language Model (LLM) to generate potential edge cases by analyzing past Gleam issues. This method successfully identified one bug, which was subsequently reported and fixed. However, the LLM-based fuzzing proved to be costly and lacked determinism, making it an inefficient long-term solution for comprehensive bug discovery.
Considering Structure-Aware Fuzzing
Following the LLM experiment, the developer is now considering structure-aware fuzzing. This technique involves generating randomized inputs that adhere to the program's expected structure, aiming to uncover edge cases that human developers might overlook. Fuzzing, in general, ranges from feeding completely random bytes to highly structured grammar-aware inputs, with a history of finding security flaws and bugs in various software.
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Primary sources
GitHub gleam-lang/gleam GitHub daniellionel01/gleam GitHub rust-fuzz/cargo-fuzz GitHub erlang/otp GitHub evanw/esbuild GitHub babel/babelReporting from
A developer explored methods for finding bugs in the Gleam compiler, including LLM-based fuzzing and structure-aware fuzzing. The initial attempt with LLMs found one bug but proved costly and non-deterministic, leading to consideration of more structured fuzzing approaches.