Automated conformance testing for JavaScript engines via deep compiler fuzzing

JavaScript (JS) is a popular, platform-independent programming language. To ensure the interoperability of JS programs across different platforms, the implementation of a JS engine should conform to the ECMAScript standard. However, doing so is challenging as there are many subtle definitions of API behaviors, and the definitions keep evolving.

We present Comfort, a new compiler fuzzing framework for detecting JS engine bugs and behaviors that deviate from the ECMAScript standard. Comfort leverages the recent advance in deep learning-based language models to automatically generate JS test code. As a departure from prior fuzzers, Comfort utilizes the well-structured ECMAScript specifications to automatically generate test data along with the test programs to expose bugs that could be overlooked by the developers or manually written test cases. Comfort then applies differential testing methodologies on the generated test cases to expose standard conformance bugs. We apply Comfort to ten mainstream JS engines. In 200 hours of automated concurrent testing runs, we discover bugs in