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Source note3 min read

Test Case Prioritization for DNNs via Neural Collapse Instability

Source

arXiv

arxiv.org/abs/2607.20046v1

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Source

Plain-Language Brief

A new method, Neural-Collapse-Inspired Prioritization (NCIP), is proposed to improve test case prioritization for deep neural networks (DNNs) in safety-critical domains where testing budgets are limited. The method uses cross-checkpoint prediction variability in the terminal training regime instead of absolute confidence, which helps in early fault discovery as DNNs can sometimes be confidently wrong. NCIP achieves strong performance in early fault discovery compared to competitive baselines across multiple datasets and architectures.

What the Document Says

  • A new test case prioritization framework, Neural-Collapse-Inspired Prioritization (NCIP), is proposed for deep neural networks (DNNs).
  • The method focuses on safety-critical domains where testing budgets are limited.
  • NCIP replaces absolute confidence with cross-checkpoint prediction variability in the terminal training regime.
  • It improves early fault discovery as DNNs can be confidently wrong and have small confidence margins between predicted and competing classes.
  • Two key components of NCIP: an equiangularity score of classifier weights and prioritization based on prediction variability across selected checkpoints.
  • The equiangularity score is quantified as the standard deviation of pairwise cosine similarities among class weight vectors.
  • Experiments show NCIP achieves strong performance in early fault discovery compared to competitive baselines across multiple datasets and architectures.
  • NCIP yields RAUC-ALL gains between 1.5% and 16.6%, and RAUC-500 gains between 4.9% and 20.6% under the same testing budget.

Key Concepts Mentioned

  • Test case prioritization
  • Deep neural networks (DNNs)
  • Safety-critical domains
  • Limited testing budgets
  • Neural-Collapse-Inspired Prioritization (NCIP)
  • Terminal training regime
  • Cross-checkpoint prediction variability
  • Confidently wrong DNNs
  • Equiangularity score
  • Class weight vectors
  • Pairwise cosine similarities
  • RAUC-ALL
  • RAUC-500

Claims Made by the Document

  • NCIP achieves strong performance in early fault discovery compared to competitive baselines.
  • NCIP yields RAUC-ALL gains between 1.5% and 16.6%, and RAUC-500 gains between 4.9% and 20.6% under the same testing budget.

Evidence Used

  • Results from extensive experiments across multiple datasets and architectures.

Methods / Approach

  • Neural-Collapse-Inspired Prioritization (NCIP) framework
  • Cross-checkpoint prediction variability
  • Equiangularity score of classifier weights
  • Selection of an NC-guided representative subset of training checkpoints
  • Prioritization based on prediction variability across selected checkpoints

Limitations Stated by the Authors

  • The paper does not provide details about the specific datasets and architectures used in the experiments.

Limitations Noted by the Agent

Agent interpretation — not from the source.

  • The study only evaluates the performance of NCIP on DNNs, leaving unclear how it would perform on other types of models.
  • The results may not be generalizable to all safety-critical domains or testing budget scenarios.

Open Questions

  • How does NCIP compare with other test case prioritization techniques for DNNs beyond the ones mentioned in the paper?
  • What are the potential implications of using NCIP on non-safety critical applications?

Notes for Future Q&A

May include interpretation. Labeled where it does.

  • It's important to note that while the study suggests strong performance, it does not necessarily imply that NCIP is always the best choice for test case prioritization in every scenario.

Raw Abstract / Excerpt

With the widespread deployment of deep neural networks (DNNs) in safety-critical domains, reducing the cost of model validation under limited testing budgets has become increasingly important. Existing test case prioritization techniques often rely on single-checkpoint confidence signals derived from output probabilities. However, DNNs can be confidently wrong, and the confidence margin between the predicted and competing classes is frequently small, which weakens early fault discovery. To address this limitation, we propose a Neural-Collapse-Inspired Prioritization (NCIP) framework that replaces absolute confidence with cross-checkpoint prediction variability in the terminal training regime, where model geometry becomes highly structured. NCIP introduces two key components. First, it selects an NC-guided representative subset of training checkpoints using an equiangularity score of classifier weights, quantified as the standard deviation of pairwise cosine similarities among class weight vectors. Second, it prioritizes test inputs by their prediction variability across the selected checkpoints, surfacing boundary-adjacent and failure-prone samples that are unstable under checkpoint-induced decision boundary shifts. Extensive experiments across multiple datasets and architectures show that NCIP achieves strong performance in early fault discovery compared with competitive baselines, with 1.5 to 16.6 percent RAUC-ALL gains and 4.9 to 20.6 percent RAUC-500 gains under the same testing budget. NCIP further attains the best average performance across all dataset-model pairs.