Automated Conformance Testing for JavaScript Engines via Deep Compiler Fuzzing
This addresses the challenge of interoperability for developers and users of JavaScript across different platforms, though it is an incremental improvement over prior fuzzing methods.
The paper tackles the problem of ensuring JavaScript engine conformance to the ECMAScript standard by introducing COMFORT, a deep compiler fuzzing framework that automatically generates test code using language models and differential testing, resulting in the discovery of 158 unique bugs across ten engines, with 115 fixed and 21 test cases added to the official test suite.
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 all tested JS engines. We had identified 158 unique JS engine bugs, of which 129 have been verified, and 115 have already been fixed by the developers. Furthermore, 21 of the Comfort-generated test cases have been added to Test262, the official ECMAScript conformance test suite.