# Concolic Testing for Deep Neural Networks

arXiv: https://arxiv.org/abs/1805.00089 · HTML: https://mlsanity.com/paper/1805.00089/

## Key facts

- Cited by 51 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Apr 2018.

## Built on by (most cited first, 30 of 51)

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- [Adversarial Robustness of Deep Neural Networks: A Survey from a Formal Verification Perspective](https://mlsanity.com/paper/2206.12227/) (2022)
- [Inspect, Understand, Overcome: A Survey of Practical Methods for AI Safety](https://mlsanity.com/paper/2104.14235/) (2021)
- [Distribution-Aware Testing of Neural Networks Using Generative Models](https://mlsanity.com/paper/2102.13602/) (2021)
- [Fairify: Fairness Verification of Neural Networks](https://mlsanity.com/paper/2212.06140/) (2022)
- [Neuro-Symbolic Verification of Deep Neural Networks](https://mlsanity.com/paper/2203.00938/) (2022)
- [POLICE: Provably Optimal Linear Constraint Enforcement for Deep Neural Networks](https://mlsanity.com/paper/2211.01340/) (2022)
- [NPC: Neuron Path Coverage via Characterizing Decision Logic of Deep Neural Networks](https://mlsanity.com/paper/2203.12915/) (2022)
- [Attack as Defense: Characterizing Adversarial Examples using Robustness](https://mlsanity.com/paper/2103.07633/) (2021)
- [A Search-Based Testing Approach for Deep Reinforcement Learning Agents](https://mlsanity.com/paper/2206.07813/) (2022)
- [Understanding Performance Problems in Deep Learning Systems](https://mlsanity.com/paper/2112.01771/) (2021)
- [QVIP: An ILP-based Formal Verification Approach for Quantized Neural Networks](https://mlsanity.com/paper/2212.11138/) (2022)
- [Uncertainty Quantification by Ensemble Learning for Computational Optical Form Measurements](https://mlsanity.com/paper/2103.01259/) (2021)
- [Sparse Adversarial Video Attacks with Spatial Transformations](https://mlsanity.com/paper/2111.05468/) (2021)
- [Abstraction and Refinement: Towards Scalable and Exact Verification of Neural Networks](https://mlsanity.com/paper/2207.00759/) (2022)
- [AequeVox: Automated Fairness Testing of Speech Recognition Systems](https://mlsanity.com/paper/2110.09843/) (2021)
- [CAISAR: A platform for Characterizing Artificial Intelligence Safety and Robustness](https://mlsanity.com/paper/2206.03044/) (2022)
- [QEBVerif: Quantization Error Bound Verification of Neural Networks](https://mlsanity.com/paper/2212.02781/) (2022)
- [Hierarchical Distribution-Aware Testing of Deep Learning](https://mlsanity.com/paper/2205.08589/) (2022)
- [Distribution Awareness for AI System Testing](https://mlsanity.com/paper/2105.02540/) (2021)
- [Fairness Testing of Deep Image Classification with Adequacy Metrics](https://mlsanity.com/paper/2111.08856/) (2021)
- [Active Assessment of Prediction Services as Accuracy Surface Over Attribute Combinations](https://mlsanity.com/paper/2108.06514/) (2021)
- [Adversarial Robustness of Deep Learning: Theory, Algorithms, and Applications](https://mlsanity.com/paper/2108.10451/) (2021)
- [Software Testing for Machine Learning](https://mlsanity.com/paper/2205.00210/) (2022)
- [Specifying and Testing k-Safety Properties for Machine-Learning Models](https://mlsanity.com/paper/2206.06054/) (2022)
- [NEUROSPF: A tool for the Symbolic Analysis of Neural Networks](https://mlsanity.com/paper/2103.00124/) (2021)
- [An Overview of Structural Coverage Metrics for Testing Neural Networks](https://mlsanity.com/paper/2208.03407/) (2022)
- [Probabilistic Risk Assessment of an Obstacle Detection System for GoA 4 Freight Trains](https://mlsanity.com/paper/2306.14814/) (2023)

## Abstract (arXiv)

Concolic testing combines program execution and symbolic analysis to explore the execution paths of a software program. This paper presents the first concolic testing approach for Deep Neural Networks (DNNs). More specifically, we formalise coverage criteria for DNNs that have been studied in the literature, and then develop a coherent method for performing concolic testing to increase test coverage. Our experimental results show the effectiveness of the concolic testing approach in both achieving high coverage and finding adversarial examples.