mlsanityData to 7 Oct 2026

NEUROSPF: A tool for the Symbolic Analysis of Neural Networks

arXiv 2103.00124 ↗First posted 27 Feb 2021cs.LG cs.AI cs.SEcited by 4

Not on the map. Why?

Abstract (arXiv)

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

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 9 of its 24 references that are in the map. First posted on arXiv 27 Feb 2021.

Builds on

9 of 24 references.

References · 9

  1. 2017Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networkscited by 739
  2. 2017DeepXplore: Automated Whitebox Testing of Deep Learning Systemscited by 307
  3. 2016Safety Verification of Deep Neural Networkscited by 262
  4. 2017DeepTest: Automated Testing of Deep-Neural-Network-driven Autonomous Carscited by 249
  5. 2018DeepGauge: Multi-Granularity Testing Criteria for Deep Learning Systemscited by 144
  6. 2018Guiding Deep Learning System Testing using Surprise Adequacycited by 123
  7. 2018Concolic Testing for Deep Neural Networkscited by 51
  8. 2020Repairing Deep Neural Networks: Fix Patterns and Challengescited by 33
  9. 2019DeepFault: Fault Localization for Deep Neural Networkscited by 29

Built on by

Cited by year

Citing papers per year: 2021: 1; 2022: 1; 2023: 2.

4 papers.

  1. 2021NNrepair: Constraint-based Repair of Neural Network Classifierscited by 24
  2. 2023FedDebug: Systematic Debugging for Federated Learning Applicationscited by 6
  3. 2022Specifying and Testing k-Safety Properties for Machine-Learning Modelscited by 5
  4. 2023DREAM: Debugging and Repairing AutoML Pipelinescited by 1