Targeted Testing of Compiler Optimizations via Grammar-Level Composition Styles

• URL: http://arxiv.org/abs/2512.04344v1
• Date: Thu, 04 Dec 2025 00:13:25 GMT
• Title: Targeted Testing of Compiler Optimizations via Grammar-Level Composition Styles
• Authors: Zitong Zhou, Ben Limpanukorn, Hong Jin Kang, Jiyuan Wang, Yaoxuan Wu, Akos Kiss, Renata Hodovan, Miryung Kim,
• Abstract summary: Existing fuzzers struggle to test compiler optimizations effectively.<n>We propose targeted fuzzing of individual optimizations to complement pipeline-based testing.<n>Our evaluation on LLVM and MLIR shows that TargetFuzz improves coverage by 8% and 11%.
• Score: 8.598686284546773
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Ensuring the correctness of compiler optimizations is critical, but existing fuzzers struggle to test optimizations effectively. First, most fuzzers use optimization pipelines (heuristics-based, fixed sequences of passes) as their harness. The phase-ordering problem can enable or preempt transformations, so pipelines inevitably miss optimization interactions; moreover, many optimizations are not scheduled, even at aggressive levels. Second, optimizations typically fire only when inputs satisfy specific structural relationships, which existing generators and mutations struggle to produce. We propose targeted fuzzing of individual optimizations to complement pipeline-based testing. Our key idea is to exploit composition styles - structural relations over program constructs (adjacency, nesting, repetition, ordering) - that optimizations look for. We build a general-purpose, grammar-based mutational fuzzer, TargetFuzz, that (i) mines composition styles from an optimization-relevant corpus, then (ii) rebuilds them inside different contexts offered by a larger, generic corpus via synthesized mutations to test variations of optimization logic. TargetFuzz is adaptable to a new programming language by lightweight, grammar-based, construct annotations - and it automatically synthesizes mutators and crossovers to rebuild composition styles. No need for hand-coded generators or language-specific mutators, which is particularly useful for modular frameworks such as MLIR, whose dialect-based, rapidly evolving ecosystem makes optimizations difficult to fuzz. Our evaluation on LLVM and MLIR shows that TargetFuzz improves coverage by 8% and 11% and triggers optimizations 2.8$\times$ and 2.6$\times$, compared to baseline fuzzers under the targeted fuzzing mode. We show that targeted fuzzing is complementary: it effectively tests all 37 sampled LLVM optimizations, while pipeline-fuzzing missed 12.

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