NEUROSPF: A tool for the Symbolic Analysis of Neural Networks

TL;DR

NEUROSPF is a tool that enables symbolic analysis of neural networks by translating them into Java for use with symbolic execution, facilitating testing, verification, and repair of neural models.

Contribution

It introduces NEUROSPF, a novel tool that converts neural networks into Java for symbolic analysis, integrating software testing techniques into machine learning model evaluation.

Findings

Enables symbolic execution of neural networks using Symbolic PathFinder.

Supports symbolic inputs or internal parameters for flexible analysis.

Facilitates adversarial example detection and network repair.

Abstract

This paper presents NEUROSPF, a tool for the symbolic analysis of neural networks. Given a trained neural network model, the tool extracts the architecture and model parameters and translates them into a Java representation that is amenable for analysis using the Symbolic PathFinder symbolic execution tool. Notably, NEUROSPF encodes specialized peer classes for parsing the model's parameters, thereby enabling efficient analysis. With NEUROSPF the user has the flexibility to specify either the inputs or the network internal parameters as symbolic, promoting the application of program analysis and testing approaches from software engineering to the field of machine learning. For instance, NEUROSPF can be used for coverage-based testing and test generation, finding adversarial examples and also constraint-based repair of neural networks, thus improving the reliability of neural networks and of the applications that use them. Video URL: https://youtu.be/seal8fG78LI