Generating Highly Structured Test Inputs Leveraging Constraint-Guided Graph Refinement

• URL: http://arxiv.org/abs/2507.21271v1
• Date: Mon, 28 Jul 2025 18:54:04 GMT
• Title: Generating Highly Structured Test Inputs Leveraging Constraint-Guided Graph Refinement
• Authors: Zhaorui Yang, Yuxin Qiu, Haichao Zhu, Qian Zhang,
• Abstract summary: This study investigates whether test inputs for structured domains can be unified through a graph-based representation.<n>We will evaluate the effectiveness of this approach in enhancing input validity and semantic preservation across eight AI systems.
• Score: 4.121384394709256
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: [Context] Modern AI applications increasingly process highly structured data, such as 3D meshes and point clouds, where test input generation must preserve both structural and semantic validity. However, existing fuzzing tools and input generators are typically handcrafted for specific input types and often generate invalid inputs that are subsequently discarded, leading to inefficiency and poor generalizability. [Objective] This study investigates whether test inputs for structured domains can be unified through a graph-based representation, enabling general, reusable mutation strategies while enforcing structural constraints. We will evaluate the effectiveness of this approach in enhancing input validity and semantic preservation across eight AI systems. [Method] We develop and evaluate GRAphRef, a graph-based test input generation framework that supports constraint-based mutation and refinement. GRAphRef maps structured inputs to graphs, applies neighbor-similarity-guided mutations, and uses a constraint-refinement phase to repair invalid inputs. We will conduct a confirmatory study across eight real-world mesh-processing AI systems, comparing GRAphRef with AFL, MeshAttack, Saffron, and two ablated variants. Evaluation metrics include structural validity, semantic preservation (via prediction consistency), and performance overhead. Experimental data is derived from ShapeNetCore mesh seeds and model outputs from systems like MeshCNN and HodgeNet. Statistical analysis and component latency breakdowns will be used to assess each hypothesis.

Related papers

• ADALog: Adaptive Unsupervised Anomaly detection in Logs with Self-attention Masked Language Model [2.55347686868565]
  ADALog is an adaptive, unsupervised anomaly detection framework.<n>It operates on individual unstructured logs, extracts intra-log contextual relationships, and performs adaptive thresholding on normal data.<n>We evaluate ADALog on benchmark datasets BGL, Thunderbird, and Spirit.
  arXiv  Detail & Related papers  (2025-05-15T17:31:40Z)
• Partial Transportability for Domain Generalization [56.37032680901525]
  Building on the theory of partial identification and transportability, this paper introduces new results for bounding the value of a functional of the target distribution.<n>Our contribution is to provide the first general estimation technique for transportability problems.<n>We propose a gradient-based optimization scheme for making scalable inferences in practice.
  arXiv  Detail & Related papers  (2025-03-30T22:06:37Z)
• Shapley-Guided Utility Learning for Effective Graph Inference Data Valuation [6.542796128290513]
  We propose Shapley-Guided Utility Learning (SGUL), a novel framework for graph inference data valuation.<n>SGUL combines transferable data-specific and modelspecific features to approximate test accuracy without relying on ground truth labels.<n>We show that SGUL consistently outperforms existing baselines in both inductive and transductive settings.
  arXiv  Detail & Related papers  (2025-03-23T20:35:03Z)
• Structural Alignment Improves Graph Test-Time Adaptation [17.564393890432193]
  We introduce Test-Time Structural Alignment (TSA), a novel algorithm for Graph Test-Time Adaptation (GTTA)<n>TSA aligns graph structures during inference without accessing the source data.<n>Experiments on synthetic and real-world datasets demonstrate TSA's consistent outperformance of both non-graph TTA methods and state-of-the-art GTTA baselines.
  arXiv  Detail & Related papers  (2025-02-25T16:26:25Z)
• F-Fidelity: A Robust Framework for Faithfulness Evaluation of Explainable AI [15.314388210699443]
  XAI techniques can extract meaningful insights from deep learning models.<n>How to properly evaluate them remains an open problem.<n>We propose Fine-tuned Fidelity (F-Fidelity) as a robust evaluation framework for XAI.
  arXiv  Detail & Related papers  (2024-10-03T20:23:06Z)
• SINDER: Repairing the Singular Defects of DINOv2 [61.98878352956125]
  Vision Transformer models trained on large-scale datasets often exhibit artifacts in the patch token they extract. We propose a novel fine-tuning smooth regularization that rectifies structural deficiencies using only a small dataset.
  arXiv  Detail & Related papers  (2024-07-23T20:34:23Z)
• Just Shift It: Test-Time Prototype Shifting for Zero-Shot Generalization with Vision-Language Models [19.683461002518147]
  Test-Time Prototype Shifting (TPS) is a pioneering approach designed to adapt vision-language models to test datasets using unlabeled test inputs.<n>TPS not only facilitates optimization-free prototype reuse for subsequent predictions but also enables seamless integration with current advancements in prompt engineering.<n>A notable aspect of our framework is its significantly reduced memory and computational demands when compared to conventional text-prompt tuning methods.
  arXiv  Detail & Related papers  (2024-03-19T17:54:34Z)
• FRGNN: Mitigating the Impact of Distribution Shift on Graph Neural Networks via Test-Time Feature Reconstruction [13.21683198528012]
  A distribution shift can adversely affect the test performance of Graph Neural Networks (GNNs) We propose FR-GNN, a general framework for GNNs to conduct feature reconstruction. Notably, the reconstructed node features can be directly utilized for testing the well-trained model.
  arXiv  Detail & Related papers  (2023-08-18T02:34:37Z)
• Energy-based Out-of-Distribution Detection for Graph Neural Networks [76.0242218180483]
  We propose a simple, powerful and efficient OOD detection model for GNN-based learning on graphs, which we call GNNSafe. GNNSafe achieves up to $17.0%$ AUROC improvement over state-of-the-arts and it could serve as simple yet strong baselines in such an under-developed area.
  arXiv  Detail & Related papers  (2023-02-06T16:38:43Z)
• Semantic Self-adaptation: Enhancing Generalization with a Single Sample [45.111358665370524]
  We propose a self-adaptive approach for semantic segmentation. It fine-tunes the parameters of convolutional layers to the input image using consistency regularization. Our empirical study suggests that self-adaptation may complement the established practice of model regularization at training time.
  arXiv  Detail & Related papers  (2022-08-10T12:29:01Z)
• Attentive Prototypes for Source-free Unsupervised Domain Adaptive 3D Object Detection [85.11649974840758]
  3D object detection networks tend to be biased towards the data they are trained on. We propose a single-frame approach for source-free, unsupervised domain adaptation of lidar-based 3D object detectors.
  arXiv  Detail & Related papers  (2021-11-30T18:42:42Z)
• Convolutional generative adversarial imputation networks for spatio-temporal missing data in storm surge simulations [86.5302150777089]
  Generative Adversarial Imputation Nets (GANs) and GAN-based techniques have attracted attention as unsupervised machine learning methods. We name our proposed method as Con Conval Generative Adversarial Imputation Nets (Conv-GAIN)
  arXiv  Detail & Related papers  (2021-11-03T03:50:48Z)
• Mitigating Generation Shifts for Generalized Zero-Shot Learning [52.98182124310114]
  Generalized Zero-Shot Learning (GZSL) is the task of leveraging semantic information (e.g., attributes) to recognize the seen and unseen samples, where unseen classes are not observable during training. We propose a novel Generation Shifts Mitigating Flow framework for learning unseen data synthesis efficiently and effectively. Experimental results demonstrate that GSMFlow achieves state-of-the-art recognition performance in both conventional and generalized zero-shot settings.
  arXiv  Detail & Related papers  (2021-07-07T11:43:59Z)
• Controlled time series generation for automotive software-in-the-loop testing using GANs [0.5352699766206808]
  Testing automotive mechatronic systems partly uses the software-in-the-loop approach, where systematically covering inputs of the system-under-test remains a major challenge. One approach is to craft input sequences which eases control and feedback of the test process but falls short of exposing the system to realistic scenarios. The other is to replay sequences recorded from field operations which accounts for reality but requires collecting a well-labeled dataset of sufficient capacity for widespread use, which is expensive. This work applies the well-known unsupervised learning framework of Generative Adrial Networks (GAN) to learn an unlabeled dataset of recorded in-vehicle
  arXiv  Detail & Related papers  (2020-02-16T16:19:29Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.