Differential Coverage-Guided Fuzzing for REST API Backward Compatibility
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Solution Overview
Problem
Conventional fuzz testing methods are inefficient in identifying regressions and ensuring backward compatibility of software applications, particularly REST APIs, due to their reliance on finite test sets and black-box approaches that do not effectively generate random input data, leading to resource-intensive and incomplete coverage.
Innovation Solution
The implementation of differential coverage-guided feedback (CGF) fuzzing systems, which generate input data based on initial samples, compare coverage information across multiple applications, and iteratively mutate inputs to expose new regressions, enabling automatic, infinite test sets and comprehensive validation of backward compatibility, including third-party components.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional fuzz testing uses finite test sets and black-box approaches, then implementation is simple, but regression identification efficiency is poor and resource consumption is high
Solution Approach 1:
The patent implements feedback mechanisms by collecting coverage information from multiple applications and using it to guide subsequent fuzzing iterations. The system compares coverage data between applications and feeds this comparison back to generate new input data, creating a closed-loop system that continuously improves testing efficiency while reducing redundant resource consumption.
Solution Approach 2:
The fuzzing system transitions from static finite test sets to dynamic infinite test set generation. Input data is continuously mutated and evolved based on coverage feedback, allowing the test set to adapt and grow indefinitely. This dynamic approach improves regression detection efficiency while avoiding the resource waste of repeatedly executing identical finite test cases.
2Reliability
If differential coverage-guided feedback fuzzing generates infinite test sets, then backward compatibility validation is comprehensive, but system complexity increases
Solution Approach 1:
The patent segments the testing system into distinct modular components: a fuzzer module for generating input data, a coverage collection module for gathering execution data, a comparison module for analyzing differences between applications, and a feedback loop for guiding subsequent iterations. This modular segmentation enables comprehensive backward compatibility validation while managing system complexity through clear separation of concerns.
Solution Approach 2:
The differential coverage-guided feedback fuzzing system serves multiple functions simultaneously: it generates infinite test sets, collects coverage information, compares multiple application versions, identifies regressions, and validates backward compatibility. This multi-functionality achieves comprehensive validation without proportionally increasing complexity, as shared infrastructure supports all functions.
3Measurement precision
If conventional fuzzing performs duplicated runs against the same code branches, then testing thoroughness is maintained, but time and resources are wasted
Solution Approach 1:
The system uses coverage feedback to identify which code branches have already been executed and prioritizes unexplored branches in subsequent iterations. By comparing coverage information between applications and feeding this back to the fuzzer, the system avoids duplicated runs on already-tested branches while maintaining comprehensive testing thoroughness, thereby reducing wasted time.
Solution Approach 2:
The patent performs preliminary coverage analysis to identify unexplored code branches before executing new test cases. By预先 (in advance) determining which branches need exploration, the system avoids time-wasting duplicated runs on already-tested paths while ensuring thorough coverage of all necessary code branches.
Data Source
AI summary
Differential coverage-guided feedback (CGF) fuzzing system and methods are provided to identify regressions in a software application. A computing device is configured to execute instructions that perform a fuzzing iteration. The fuzzing iteration includes operations that generate input data based on an initial corpus of samples; communicate the input data to a first application such that the first application performs operations utilizing the input data; collect first coverage information from the first application to identify first regressions; communicate the input data to a second application such that the second application performs operations utilizing the input data; and collect second coverage information from the first application to identify second regressions. The instructions additionally include: compare the first coverage information and the second coverage information; and perform another fuzzing iteration, wherein the computing device is configured to execute instructions that generate input data based on the compared first and second coverage information.