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Concolic Testing for Deep Neural Networks

arXiv 1805.00089 ↗First posted 30 Apr 2018cs.LG cs.SE stat.MLcited by 51

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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.

Key facts. Cited by 51 papers in the map (data through 7 Oct 2026). First posted on arXiv 30 Apr 2018.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 18; 2022: 16; 2023: 10; 2024: 3; 2025: 3; 2026: 1.

51 papers.

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  1. 2023A Survey of Safety and Trustworthiness of Large Language Models through the Lens of Verification and Validationcited by 91
  2. 2021Software Engineering for AI-Based Systems: A Surveycited by 68
  3. 2022Adversarial Robustness of Deep Neural Networks: A Survey from a Formal Verification Perspectivecited by 41
  4. 2021Provable Repair of Deep Neural Networkscited by 39
  5. 2021Inspect, Understand, Overcome: A Survey of Practical Methods for AI Safetycited by 35
  6. 2021Distribution-Aware Testing of Neural Networks Using Generative Modelscited by 22
  7. 2022Fairify: Fairness Verification of Neural Networkscited by 22
  8. 2022Neuro-Symbolic Verification of Deep Neural Networkscited by 21
  9. 2022POLICE: Provably Optimal Linear Constraint Enforcement for Deep Neural Networkscited by 19
  10. 2022NPC: Neuron Path Coverage via Characterizing Decision Logic of Deep Neural Networkscited by 17
  11. 2021Attack as Defense: Characterizing Adversarial Examples using Robustnesscited by 14
  12. 2021Understanding Performance Problems in Deep Learning Systemscited by 12
38 more
  1. 2022A Search-Based Testing Approach for Deep Reinforcement Learning Agentscited by 12
  2. 2022QVIP: An ILP-based Formal Verification Approach for Quantized Neural Networkscited by 12
  3. 2021Uncertainty Quantification by Ensemble Learning for Computational Optical Form Measurementscited by 11
  4. 2021Sparse Adversarial Video Attacks with Spatial Transformationscited by 11
  5. 2022Abstraction and Refinement: Towards Scalable and Exact Verification of Neural Networkscited by 11
  6. 2021AequeVox: Automated Fairness Testing of Speech Recognition Systemscited by 9
  7. 2022CAISAR: A platform for Characterizing Artificial Intelligence Safety and Robustnesscited by 9
  8. 2022QEBVerif: Quantization Error Bound Verification of Neural Networkscited by 9
  9. 2022Hierarchical Distribution-Aware Testing of Deep Learningcited by 8
  10. 2021Distribution Awareness for AI System Testingcited by 6
  11. 2021Fairness Testing of Deep Image Classification with Adequacy Metricscited by 6
  12. 2021Active Assessment of Prediction Services as Accuracy Surface Over Attribute Combinationscited by 5
  13. 2021Adversarial Robustness of Deep Learning: Theory, Algorithms, and Applicationscited by 5
  14. 2022Software Testing for Machine Learningcited by 5
  15. 2022Specifying and Testing k-Safety Properties for Machine-Learning Modelscited by 5
  16. 2021NEUROSPF: A tool for the Symbolic Analysis of Neural Networkscited by 4
  17. 2022An Overview of Structural Coverage Metrics for Testing Neural Networkscited by 3
  18. 2023Probabilistic Risk Assessment of an Obstacle Detection System for GoA 4 Freight Trainscited by 3
  19. 2023Neuron Sensitivity Guided Test Case Selection for Deep Learning Testingcited by 3
  20. 2024FAST: Boosting Uncertainty-based Test Prioritization Methods for Neural Networks via Feature Selectioncited by 3
  21. 2021Machine Learning Application Development: Practitioners' Insightscited by 2
  22. 2021Abstraction and Symbolic Execution of Deep Neural Networks with Bayesian Approximation of Hidden Featurescited by 1
  23. 2022Is Neuron Coverage Needed to Make Person Detection More Robust?cited by 1
  24. 2023Feature Map Testing for Deep Neural Networkscited by 1
  25. 2023The Pros and Cons of Adversarial Robustnesscited by 1
  26. 2023DREAM: Debugging and Repairing AutoML Pipelinescited by 1
  27. 2024ARQ: A Mixed-Precision Quantization Framework for Accurate and Certifiably Robust DNNscited by 1
  28. 2021Testing Autonomous Systems with Believed Equivalence Refinementcited by 0
  29. 2021Automated Testing of AI Modelscited by 0
  30. 2022Safe AI -- How is this Possible?cited by 0
  31. 2023Understanding the Complexity and Its Impact on Testing in ML-Enabled Systemscited by 0
  32. 2023QNNRepair: Quantized Neural Network Repaircited by 0
  33. 2023On the Relationship Between RNN Hidden State Vectors and Semantic Ground Truthcited by 0
  34. 2023Good Tools are Half the Work: Tool Usage in Deep Learning Projectscited by 0
  35. 2024DeepCover: Advancing RNN Test Coverage and Online Error Prediction using State Machine Extractioncited by 0
  36. 2025Anomaly Detection Based on Critical Paths for Deep Neural Networkscited by 0
  37. 2025Influence-Guided Concolic Testing of Transformer Robustnesscited by 0
  38. 2025Attack logics, not outputs: Towards efficient robustification of deep neural networks by falsifying concept-based propertiescited by 0

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