SafeAI 2022: Keynote on AI Safety by Matthew Dwyer

SafeAI 2022: Keynote on AI Safety by Matthew Dwyer

Table of Contents:

1. Introduction
2. Background on Traditional Software Testing 2.1 Requirements and Specifications 2.2 Control and Data Flow in Traditional Software Systems 2.3 Test Inputs and Test Oracles 2.4 High-Level Goals of Testing
3. Test Adequacy Criteria for Traditional Software Systems 3.1 Black Box Adequacy 3.2 White Box Adequacy 3.3 Challenges in Applying White Box Adequacy to Neural Networks
4. Distribution-Aware Black Box Test Adequacy for Neural Networks 4.1 Understanding Data Distribution in Neural Networks 4.2 Feasible Feature Vectors and Feature Abstraction 4.3 Feature Partitioning and Coverage Domain 4.4 Combinatorial Interaction Testing for Coverage Measurement
5. Performance and Comparison with Existing Metrics 5.1 Measuring Feature Variation with IDC 5.2 Comparison with White Box Metrics
6. Future Work and Potential Applications
7. Conclusion

Introduction

Neural networks have become increasingly popular in various domains, often replacing traditional software systems. Testing neural networks poses unique challenges due to the lack of requirement specifications and the different structure of these systems. While black box and white box test adequacy criteria have been developed for traditional software, they may not be suitable for neural networks. In this article, we explore the concept of distribution-aware black box test adequacy for neural networks, which focuses on understanding the data distribution and designing effective test suites. We compare this approach with existing metrics and discuss its potential applications.

Background on Traditional Software Testing

Traditional software systems are typically developed Based on requirement specifications. These systems consist of multiple components programmed by humans using programming languages. Requirements describe the expected outputs for a given input, and developers implement these components accordingly. Testing traditional software involves creating test inputs and test oracles, where a test input is a pair of input values, and a test oracle defines the expected outputs based on the requirements. The goal of testing is to provide evidence that the system meets its requirements and is free of faults.

Pros:

• Clearly defined requirements
• Existing methodologies and frameworks for testing

Cons:

• Lack of adaptability to changing environments
• Limited coverage due to manual test case creation

Test Adequacy Criteria for Traditional Software Systems

Test adequacy criteria aim to measure the effectiveness of a testing process by defining coverage metrics. Two common frameworks are black box adequacy and white box adequacy.

Black Box Adequacy Black box adequacy focuses on the input space of the system. Since exhaustively covering the entire input space is often not feasible, the input space is abstracted based on the structure of the requirements. This abstraction allows for effective coverage measurement. Common criteria include coverage of active neurons, neuron ranges, and neuron boundaries.

White Box Adequacy White box adequacy focuses on the control and data flow within the system's implementation. Test coverage is measured based on the coverage of statements, branches, or combinations thereof. The challenge with applying white box adequacy to neural networks is that the control information is not explicitly present in these systems, making traditional coverage metrics less effective.

Distribution-Aware Black Box Test Adequacy for Neural Networks

In the Context of neural networks, distribution-aware black box test adequacy focuses on understanding the data distribution and designing test suites that effectively cover it.

1. Understanding Data Distribution: Neural networks are data-driven systems, and the data they operate on often resides on low-dimensional manifolds within high-dimensional space. By leveraging techniques like variational autoencoders (VAEs), we can learn the feature space and encode inputs into feasible feature vectors.

2. Feasible Feature Vectors and Abstraction: Feasible feature vectors are derived from the latent representations learned by VAEs. Feature abstraction involves analyzing these feature vectors and determining which Dimensions are noise dimensions and which represent Meaningful generative factors.

3. Feature Partitioning and Coverage Domain: Non-noise dimensions are further partitioned to define a coverage domain. This partitioning enables effective coverage measurement based on combinatorial interaction testing (CIT) metrics.

4. Combinatorial Interaction Testing: CIT metrics capture the coverage of combinations of features within the defined coverage domain. While traditional CIT assumes feature independence, adapting it to neural networks requires handling non-linear constraints introduced by the distribution-aware approach.

Performance and Comparison with Existing Metrics

In comparative studies with existing white box metrics, distribution-aware black box test adequacy (IDC) demonstrates superior performance in measuring feature variation within test suites for neural networks. While traditional metrics like neuron coverage may fail to detect meaningful differences, IDC consistently detects new coverage across various test generation techniques. By combining white box and black box testing approaches, we can gain a more comprehensive understanding of test suite effectiveness.

Future Work and Potential Applications

Further research is ongoing to exploit the decoder component of variational autoencoders to support test generation. This approach aims to generate a minimal test suite that guarantees a given level of coverage within the defined coverage domain. By fine-tuning the parameters of target density, combinatorial strength, and partitioning scheme, we can balance coverage effectiveness and cost in critical deployment scenarios.

Potential applications of distribution-aware black box test adequacy include testing neural networks used in safety-critical systems, autonomous vehicles, medical diagnosis systems, and other domains that demand high levels of testing confidence.

Conclusion

Distribution-aware black box test adequacy for neural networks presents a Novel approach to measure the effectiveness of test suites. By leveraging the data distribution and designing test cases that cover the feature space, we can gain Better Insights into how well neural networks perform in meeting requirements and detecting faults. The combination of black box and white box testing approaches offers a holistic approach to testing neural networks in critical deployments, ensuring better coverage and reducing the risk of undetected faults. Ongoing research in this area will further enhance the effectiveness and efficiency of testing neural networks.