Researcher Uses Fuzzing to Hunt for Gleam Compiler Discrepancies

An independent researcher is employing automated fuzzing to identify bugs within the Gleam compiler. By generating random programs, the researcher aims to uncover edge cases and discrepancies in how the compiler handles code generation across different platforms.

The project specifically targets potential divergent behavior between Gleam's two primary targets: Erlang (BEAM) and JavaScript. The researcher initially experimented with Large Language Models (LLMs) to generate test cases before transitioning toward more structured fuzzing methodologies. The objective is to find instances where the same Gleam source code produces different outputs or behaviors depending on the chosen runtime.

The Challenge of Dual Targets

Gleam is a type-safe functional language designed to be portable across diverse environments. Because it compiles to both the Erlang virtual machine and JavaScript, it must map high-level semantics to two fundamentally different execution models. Ensuring that a program behaves identically regardless of the target is a critical challenge for maintainers, as the underlying runtimes handle memory, concurrency, and types in distinct ways.

Why Automated Fuzzing Matters

Bugs in compiler code generation are notoriously difficult to detect because they often manifest as subtle runtime errors that appear on only one specific target. Traditional manual testing relies on a developer's ability to anticipate problematic patterns, which often leaves rare edge cases undiscovered. Automated fuzzing provides a scalable alternative, allowing the researcher to stress-test the compiler with a volume and variety of programs that would be impossible to write by hand.

The motivation for this approach stems from a recurring frustration with code generation issues. When discrepancies arise between Erlang and JavaScript outputs, it suggests a need for a system that can essentially "compute all the Gleam programs" to determine if other issues remain hidden.

Next Steps for Compiler Stability

While initial exploration has established the viability of random program generation, the focus now shifts toward refining structured fuzzing approaches to increase the hit rate of meaningful bugs. This project highlights a broader trend in language development where differential testing—comparing the output of two different implementations of the same specification—is used to ensure correctness in complex compilers. By systematically isolating these discrepancies, the researcher hopes to contribute to the overall stability and reliability of the Gleam ecosystem.