Title: Fuzzing MLIR by Synthesizing Custom Mutations

Abstract: Multi-Level Intermediate Representation (MLIR) is an effort to enable faster compiler development by providing an extensible framework for downstream developers to define custom IRs with MLIR dialects. MLIR dialects define new IRs that are tailored for specific domains. The diversity and rapid evolution of these IRs make it impractical to pre-define custom generator logic for every available dialect. We design a new approach called SynthFuzz that automatically infers and applies custom mutations from existing tests. Inferred custom mutations are parameterized and context-dependent such that they can be concretized depending on the target context. By doing this, we obviate the need to manually write custom mutations for newly introduced MLIR dialects. Further, SynthFuzz increases the chance of finding effective edit locations and reduces the chance of inserting invalid edit content by performing k-ancestor-prefix and l-sibling-postfix matching. We compare SynthFuzz to three baselines: Grammarinator -- a grammar-based fuzzer without custom mutators, MLIRSmith -- a custom test generator for MLIR, and NeuRI -- a custom test generator with support for parameterized generation. We conduct this comparison on 4 different MLIR projects where each project defines a new set of MLIR dialects that would take months of effort to manually write custom input generation and mutation logic. We show that SynthFuzz on average improves input diversity by 1.51$\times$, which increases branch coverage by 1.16$\times$. Further, we show that our context dependent custom mutation increases the proportion of valid tests by up to 1.11$\times$, indicating that SynthFuzz correctly concretizes its parameterized mutations with respect to the target context. Mutation parameterization reduces the fraction of tests violating general MLIR constraints by 0.57$\times$, increasing the time spent fuzzing dialect-specific code.