Neural Network Debugging with Coverage-Guided Fuzzing
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Solution Overview
Problem
Machine learning models, particularly neural networks, are difficult to debug and interpret, necessitating effective methods to discover bugs and undesirable behavior before deployment.
Innovation Solution
Applying coverage guided fuzzing to neural networks, utilizing a seed corpus of inputs and guided mutations based on coverage metrics, including activation vector analysis and objective functions to identify new coverage and errors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional testing methods are used on neural networks, then the testing process is simple to implement, but the ability to discover bugs and undesirable behavior is limited
Solution Approach 1:
The patent introduces an intermediate coverage metric system that mediates between the test inputs and the neural network internal states. The coverage metric computes activation vectors and compares them against a coverage graph to determine which parts of the network have been exercised, providing a bridge that enables systematic bug detection without requiring complete understanding of the network's complex internal behavior
Solution Approach 2:
The patent performs preliminary actions by building a coverage graph that maps expected activation patterns before actual testing begins. This pre-computed coverage metric serves as a guide for generating test cases, allowing the system to proactively identify which network regions need to be tested and what bugs might be present, rather than reacting to failures after they occur
2Productivity
If coverage guided fuzzing is applied to neural networks, then debugging efficiency is improved, but the computational complexity increases
Solution Approach 1:
The patent implements feedback by continuously computing the coverage metric from activation vectors and using this information to guide the fuzzing process. The coverage graph compares expected versus actual activation patterns, providing feedback that directs which test cases should be generated next and which parts of the network require additional testing, thereby improving debugging efficiency in an iterative manner
Solution Approach 2:
The patent applies partial action by focusing coverage analysis on specific critical regions of the neural network rather than attempting to analyze every single parameter. The coverage metric selectively tracks activation vectors in key layers and operations, providing sufficient debugging information without requiring complete exhaustive analysis of the entire network, thus balancing efficiency with computational feasibility
3Reliability
If systematic testing of neural networks is performed, then bug detection capability is enhanced, but the time required for testing increases
Solution Approach 1:
The patent performs preliminary action by pre-computing the coverage graph that contains expected activation patterns for different test cases. This preparation work is done before actual systematic testing begins, allowing the coverage metric to quickly compare actual activations against pre-stored expectations during testing, thereby enhancing bug detection capability without proportionally increasing testing time
Solution Approach 2:
The patent substitutes traditional mechanical systematic testing approaches with a coverage-guided intelligent system. Instead of exhaustively testing all possible inputs in a predetermined sequence, the system uses coverage metrics to intelligently select which test cases provide the most value, replacing brute-force systematic testing with a smarter, adaptive approach that achieves better bug detection in less time
Data Source
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AI summary
The present disclosure provides systems and methods for debugging neural networks. In one example, a computer-implemented method is provided, which includes obtaining, by one or more computing devices, one or more inputs from an input corpus. The method further includes mutating, by the one or more computing devices, the one or more inputs and providing the one or more mutated inputs to a neural network; obtaining, by the one or more computing devices as a result of the neural network processing the one or more mutated inputs, a set of coverage arrays; determining, by the one or more computing devices based at least in part on the set of coverage arrays, whether the one or more mutated inputs provide new coverage; and upon determining that the one or more mutated inputs provide new coverage, adding the one or more mutated inputs to the input corpus.