arXiv AI
FuzzInsight: Navigating the Efficiency-Effectiveness Trade-off in Coverage-Guided DNN Fuzzing
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Latent Anchor-Driven Test Generation for Deep Neural Networks
arXiv:2606. 04310v1 Announce Type: new Abstract: Deep Neural Networks (DNNs) are increasingly being deployed in security-critical and safety-sensitive applications, which makes rigorous testing essential to identify and mitigate model weaknesses.
arXiv Machine Learning
Aug 13
ADEPT: A Unified Framework for Deep Learning Test Adequacy
arXiv:2608. 12144v1 Announce Type: cross Abstract: Over the past decade, many test adequacy metrics have been proposed for deep learning that characterize test dataset adequacy from different perspectives, e.
arXiv Machine Learning
Jul 20
Learning Faster without Deeper Networks: A*-Inspired Batch Selection for Efficient CNN Training
arXiv:2607. 15745v1 Announce Type: new Abstract: Common practice when training Convolutional Neural Networks (CNNs) is to use randomly shuffled mini-batches.
arXiv AI
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SWE-Test: Benchmarking LLM Vulnerability Discovery via Input Prediction
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Jun 29
Test-Input Generation for Tensor Programs: What Actually Finds Kernel Bugs
arXiv:2606. 27396v1 Announce Type: cross Abstract: Test-input generation for tensor kernels is folkloric.
arXiv Machine Learning
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Stateful Testing for Deep Learning Compilers
The paper introduces Rondo, a stateful testing framework for deep‑learning compilers that models each test as a model plus an invocation sequence. Rondo mutates sequences using a Markov chain Monte Carlo sampler guided by compilation behavior, and employs a three‑way differential oracle to detect bugs that arise only when compiler state is retained. Evaluated on four major frameworks and 24 model architectures, Rondo uncovered 117 bugs—35 of which were stateful—leading to 39 bug reports, 35 confirmed by developers, and 18 fixes, while achieving high coverage and precision.