VERIFAI: A Toolkit for the Design and Analysis of Artificial Intelligence-Based Systems
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Abstract (arXiv)
We present VERIFAI, a software toolkit for the formal design and analysis of systems that include artificial intelligence (AI) and machine learning (ML) components. VERIFAI particularly seeks to address challenges with applying formal methods to perception and ML components, including those based on neural networks, and to model and analyze system behavior in the presence of environment uncertainty. We describe the initial version of VERIFAI which centers on simulation guided by formal models and specifications. Several use cases are illustrated with examples, including temporal-logic falsification, model-based systematic fuzz testing, parameter synthesis, counterexample analysis, and data set augmentation.
Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). First posted on arXiv 12 Feb 2019.
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Citing papers per year: 2021: 1; 2022: 3; 2023: 2.
6 papers.
- 2021Neural Network Guided Evolutionary Fuzzing for Finding Traffic Violations of Autonomous Vehiclescited by 19
- 2023Distributionally Robust Statistical Verification with Imprecise Neural Networkscited by 12
- 2022Monitoring of Perception Systems: Deterministic, Probabilistic, and Learning-based Fault Detection and Identificationcited by 6
- 2022Checking Trustworthiness of Probabilistic Computations in a Typed Natural Deduction Systemcited by 3
- 2022Exploring Effects of Computational Parameter Changes to Image Recognition Systemscited by 3
- 2023DeltaNN: Assessing the Impact of Computational Environment Parameters on the Performance of Image Recognition Modelscited by 3
- 2023Distributionally Robust Statistical Verification with Imprecise Neural Networks28 Aug 2023
- 2023DeltaNN: Assessing the Impact of Computational Environment Parameters on the Performance of Image Recognition Models5 Jun 2023
- 2022Exploring Effects of Computational Parameter Changes to Image Recognition Systems1 Nov 2022
- 2022Checking Trustworthiness of Probabilistic Computations in a Typed Natural Deduction System26 Jun 2022
- 2022Monitoring of Perception Systems: Deterministic, Probabilistic, and Learning-based Fault Detection and Identification22 May 2022
- 2021Neural Network Guided Evolutionary Fuzzing for Finding Traffic Violations of Autonomous Vehicles13 Sep 2021