# RobOT: Robustness-Oriented Testing for Deep Learning Systems

arXiv: https://arxiv.org/abs/2102.05913 · HTML: https://mlsanity.com/paper/2102.05913/

## Key facts

- Cited by 27 papers in the map (data through 7 Oct 2026).
- First posted on arXiv 11 Feb 2021.

## Built on by (most cited first, 27 of 27)

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- [Simulator-based explanation and debugging of hazard-triggering events in DNN-based safety-critical systems](https://mlsanity.com/paper/2204.00480/) (2022)
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- [Reliability Assurance for Deep Neural Network Architectures Against Numerical Defects](https://mlsanity.com/paper/2302.06086/) (2023)
- [Neuron Sensitivity Guided Test Case Selection for Deep Learning Testing](https://mlsanity.com/paper/2307.11011/) (2023)
- [An Image is Worth a Thousand Toxic Words: A Metamorphic Testing Framework for Content Moderation Software](https://mlsanity.com/paper/2308.09810/) (2023)
- [FAST: Boosting Uncertainty-based Test Prioritization Methods for Neural Networks via Feature Selection](https://mlsanity.com/paper/2409.09130/) (2024)
- [Robust Active Learning: Sample-Efficient Training of Robust Deep Learning Models](https://mlsanity.com/paper/2112.02542/) (2021)
- [QuanTest: Entanglement-Guided Testing of Quantum Neural Network Systems](https://mlsanity.com/paper/2402.12950/) (2024)
- [How Does Bayes Error Limit Probabilistic Robust Accuracy](https://mlsanity.com/paper/2405.14923/) (2024)
- [Feature Map Testing for Deep Neural Networks](https://mlsanity.com/paper/2307.11563/) (2023)
- [Excitement Surfeited Turns to Errors: Deep Learning Testing Framework Based on Excitable Neurons](https://mlsanity.com/paper/2202.07464/) (2022)
- [Hazards in Deep Learning Testing: Prevalence, Impact and Recommendations](https://mlsanity.com/paper/2309.05381/) (2023)
- [Generating Realistic, Diverse, and Fault-Revealing Inputs with Latent Space Interpolation for Testing Deep Neural Networks](https://mlsanity.com/paper/2503.17630/) (2025)
- [Towards Assessing Deep Learning Test Input Generators](https://mlsanity.com/paper/2504.02329/) (2025)
- [VLAMotor: Test-Guided Enhancement of Vision-Language-Action Models via Agent-BasedData Synthesis](https://mlsanity.com/paper/2606.00053/) (2026)
- [Testing Neural Networks via Bayesian-Guided Exploration of Decision Landscapes](https://mlsanity.com/paper/2606.04314/) (2026)

## Abstract (arXiv)

Recently, there has been a significant growth of interest in applying software engineering techniques for the quality assurance of deep learning (DL) systems. One popular direction is deep learning testing, where adversarial examples (a.k.a.~bugs) of DL systems are found either by fuzzing or guided search with the help of certain testing metrics. However, recent studies have revealed that the commonly used neuron coverage metrics by existing DL testing approaches are not correlated to model robustness. It is also not an effective measurement on the confidence of the model robustness after testing. In this work, we address this gap by proposing a novel testing framework called Robustness-Oriented Testing (RobOT). A key part of RobOT is a quantitative measurement on 1) the value of each test case in improving model robustness (often via retraining), and 2) the convergence quality of the model robustness improvement. RobOT utilizes the proposed metric to automatically generate test cases valuable for improving model robustness. The proposed metric is also a strong indicator on how well robustness improvement has converged through testing. Experiments on multiple benchmark datasets confirm the effectiveness and efficiency of RobOT in improving DL model robustness, with 67.02% increase on the adversarial robustness that is 50.65% higher than the state-of-the-art work DeepGini.