Automatically Synthesizing Custom Mutations to Fuzz Extensible Compiler Intermediate Representations

Conceitos Básicos

SYNTHFUZZ automatically synthesizes and applies custom, context-dependent mutations to generate diverse and valid test cases for rapidly evolving compiler intermediate representations (IRs) like MLIR.

Resumo

The paper presents SYNTHFUZZ, a novel compiler fuzzing technique that addresses the challenge of generating valid test cases for rapidly evolving compiler intermediate representations (IRs) like MLIR. MLIR enables modular extensions through custom dialects, making it impractical to pre-define custom test generators for every new dialect.
SYNTHFUZZ overcomes this challenge by automatically synthesizing parameterized mutations from existing test cases. The key idea is to extract a parameterized mutation and a parameterized context from a donor test case, and then concretize the mutation to match the context of a recipient test case. This allows SYNTHFUZZ to generate valid test cases without manual effort to define custom mutations for new dialects.
The evaluation compares SYNTHFUZZ against baseline fuzzers like Grammarinator, MLIRSmith, and NeuRI on four MLIR-based compiler projects. The results show that SYNTHFUZZ achieves 1.16x greater branch coverage and 1.51x greater dialect coverage on average, without requiring any hand-coded custom generator logic. SYNTHFUZZ also reduces the proportion of test cases violating general MLIR constraints by 0.57x, allowing more time to be spent fuzzing dialect-specific code. Additionally, SYNTHFUZZ discovered a previously unknown bug in the CIRCT project, which was promptly fixed by the developers.

Estatísticas

SYNTHFUZZ improves branch coverage by up to 1.51x compared to baseline fuzzers.
SYNTHFUZZ achieves 1.50x and 1.43x improvement in control-dependent and data-dependent dialect pair coverage, respectively, compared to baseline fuzzers.
Increasing the context matching requirements (k-ancestors, l-siblings, r-siblings) improves the proportion of valid test cases by up to 1.11x.
Parameterization of mutations reduces the proportion of test cases violating general MLIR constraints by 0.57x.

Citações

"SYNTHFUZZ automatically infers and applies custom mutations from existing tests. The key essence of SYNTHFUZZ is that inferred custom mutations are parameterized and context-dependent such that they can be concretized differently depending on the target context."
"By doing this, we obviate the need to manually write custom mutations for newly introduced MLIR dialects."

Principais Insights Extraídos De

Fuzzing MLIR by Synthesizing Custom Mutations

by Ben Limpanuk... às arxiv.org 04-29-2024

https://arxiv.org/pdf/2404.16947.pdf

Perguntas Mais Profundas

How can SYNTHFUZZ's mutation synthesis be extended to handle more complex relationships between operations, such as data flow analysis or control flow analysis?

SYNTHFUZZ's mutation synthesis can be extended to handle more complex relationships between operations by incorporating advanced analysis techniques such as data flow analysis and control flow analysis.

Data Flow Analysis:

Parameterized Data Dependencies: SYNTHFUZZ can be enhanced to capture data dependencies between operations. By parameterizing data dependencies, SYNTHFUZZ can ensure that the generated mutations maintain the correct data flow relationships between variables and operations.
Data Flow Constraints: Introducing constraints based on data flow analysis results can guide the mutation synthesis process. SYNTHFUZZ can analyze the data flow within the code and use this information to generate mutations that respect the data dependencies.

Control Flow Analysis:

Parameterized Control Dependencies: Similar to data dependencies, SYNTHFUZZ can parameterize control dependencies between operations. This would involve capturing the control flow relationships within the code and ensuring that mutations preserve these dependencies.
Control Flow Constraints: By incorporating control flow analysis results, SYNTHFUZZ can generate mutations that adhere to the specific control flow patterns present in the code. This can help in creating test cases that cover different control flow scenarios.

Advanced Context Matching:

Enhanced Context Matching: SYNTHFUZZ can improve its context matching algorithms to consider both data flow and control flow information. By analyzing the relationships between operations in terms of both data and control flow, SYNTHFUZZ can identify suitable mutation locations that respect these complex dependencies.

Integration with Analysis Tools:

Integration with Static Analysis Tools: SYNTHFUZZ can leverage the results of static analysis tools that perform data flow and control flow analysis. By integrating with these tools, SYNTHFUZZ can obtain detailed information about the code's behavior and use it to guide the mutation synthesis process.

By incorporating data flow analysis, control flow analysis, and advanced context matching techniques, SYNTHFUZZ can handle more intricate relationships between operations, leading to the generation of test cases that cover a wider range of scenarios and dependencies within the code.

How could SYNTHFUZZ's parameterized mutations be leveraged to generate targeted test cases for specific compiler optimizations or bug patterns?

SYNTHFUZZ's parameterized mutations can be leveraged to generate targeted test cases for specific compiler optimizations or bug patterns by customizing the mutation synthesis process to focus on the optimization techniques or bug patterns of interest. Here are some ways to achieve this:

Optimization-Specific Mutations:

Parameterization for Optimization Patterns: SYNTHFUZZ can be configured to parameterize mutations based on known optimization patterns. For example, if a specific optimization technique involves code transformations like loop unrolling or constant propagation, SYNTHFUZZ can generate mutations that target these patterns.
Contextual Mutation Generation: By analyzing the code regions where optimizations are typically applied, SYNTHFUZZ can generate mutations that mimic the transformations involved in those optimizations. Parameterizing the mutations based on the optimization context can lead to the creation of targeted test cases.

Bug Pattern Detection:

Parameterization for Bug Scenarios: SYNTHFUZZ can be adapted to identify common bug patterns in the codebase. By parameterizing mutations to trigger these bug patterns, SYNTHFUZZ can generate test cases that expose vulnerabilities or errors related to specific bug scenarios.
Mutation Templates for Bug Detection: Creating mutation templates that correspond to known bug patterns can help SYNTHFUZZ generate test cases that are designed to uncover these issues. Parameterizing the mutations within these templates can facilitate the generation of targeted test cases for bug detection.

Feedback-Driven Mutation Generation:

Feedback Loop with Optimization/Bug Detection Tools: SYNTHFUZZ can establish a feedback loop with optimization tools or bug detection mechanisms. By receiving feedback on the effectiveness of generated test cases in triggering optimizations or exposing bugs, SYNTHFUZZ can adapt its parameterized mutations to focus on the areas that require further testing or optimization.

Domain-Specific Mutation Libraries:

Custom Mutation Libraries: Developing domain-specific mutation libraries that encapsulate optimization strategies or bug patterns can enhance SYNTHFUZZ's ability to generate targeted test cases. These libraries can provide a set of predefined parameterized mutations tailored to specific optimization or bug scenarios.

By tailoring SYNTHFUZZ's parameterized mutations to specific compiler optimizations or bug patterns, developers can efficiently generate test cases that are designed to validate the effectiveness of optimizations or uncover potential vulnerabilities in the codebase.