An Empirical Study of Fault Localisation Techniques for Deep Learning

• URL: http://arxiv.org/abs/2412.11304v2
• Date: Tue, 17 Dec 2024 10:07:46 GMT
• Title: An Empirical Study of Fault Localisation Techniques for Deep Learning
• Authors: Nargiz Humbatova, Jinhan Kim, Gunel Jahangirova, Shin Yoo, Paolo Tonella,
• Abstract summary: We evaluate and compare existing state-of-the-art fault localisation techniques. dfd is the most effective tool, achieving an average recall of 0.61 and precision of 0.41 on our benchmark.
• Score: 17.586333091528594
• License:
• Abstract: With the increased popularity of Deep Neural Networks (DNNs), increases also the need for tools to assist developers in the DNN implementation, testing and debugging process. Several approaches have been proposed that automatically analyse and localise potential faults in DNNs under test. In this work, we evaluate and compare existing state-of-the-art fault localisation techniques, which operate based on both dynamic and static analysis of the DNN. The evaluation is performed on a benchmark consisting of both real faults obtained from bug reporting platforms and faulty models produced by a mutation tool. Our findings indicate that the usage of a single, specific ground truth (e.g., the human defined one) for the evaluation of DNN fault localisation tools results in pretty low performance (maximum average recall of 0.31 and precision of 0.23). However, such figures increase when considering alternative, equivalent patches that exist for a given faulty DNN. Results indicate that \dfd is the most effective tool, achieving an average recall of 0.61 and precision of 0.41 on our benchmark.

Related papers

• Improved Detection and Diagnosis of Faults in Deep Neural Networks Using Hierarchical and Explainable Classification [3.2623791881739033]
  We present DEFault -- a novel technique to detect and diagnose faults in Deep Neural Networks (DNN) programs. Our approach achieves 94% recall in detecting real-world faulty DNN programs and 63% recall in diagnosing the root causes of the faults, demonstrating 3.92% - 11.54% higher performance than that of state-of-the-art techniques.
  arXiv  Detail & Related papers  (2025-01-22T00:55:09Z)
• Online GNN Evaluation Under Test-time Graph Distribution Shifts [92.4376834462224]
  A new research problem, online GNN evaluation, aims to provide valuable insights into the well-trained GNNs's ability to generalize to real-world unlabeled graphs. We develop an effective learning behavior discrepancy score, dubbed LeBeD, to estimate the test-time generalization errors of well-trained GNN models.
  arXiv  Detail & Related papers  (2024-03-15T01:28:08Z)
• Mutation-based Fault Localization of Deep Neural Networks [11.93979764176335]
  Deep neural networks (DNNs) are susceptible to bugs, just like other types of software systems. This paper revisits mutation-based fault localization in the context of DNN models and proposes a novel technique. Deepmufl detects 53/109 of the bugs by ranking the buggy layer in top-1 position.
  arXiv  Detail & Related papers  (2023-09-10T16:18:49Z)
• Instance-based Learning with Prototype Reduction for Real-Time Proportional Myocontrol: A Randomized User Study Demonstrating Accuracy-preserving Data Reduction for Prosthetic Embedded Systems [0.0]
  This work presents the design, implementation and validation of learning techniques based on the kNN scheme for gesture detection in prosthetic control. The influence of parameterization and varying proportionality schemes is analyzed, utilizing an eight-channel-sEMG armband.
  arXiv  Detail & Related papers  (2023-08-21T20:15:35Z)
• Bridging Precision and Confidence: A Train-Time Loss for Calibrating Object Detection [58.789823426981044]
  We propose a novel auxiliary loss formulation that aims to align the class confidence of bounding boxes with the accurateness of predictions. Our results reveal that our train-time loss surpasses strong calibration baselines in reducing calibration error for both in and out-domain scenarios.
  arXiv  Detail & Related papers  (2023-03-25T08:56:21Z)
• DeepVigor: Vulnerability Value Ranges and Factors for DNNs' Reliability Assessment [1.189955933770711]
  Deep Neural Networks (DNNs) and their accelerators are being deployed more frequently in safety-critical applications. We propose a novel accurate, fine-grain, metric-oriented, and accelerator-agnostic method called DeepVigor.
  arXiv  Detail & Related papers  (2023-03-13T08:55:10Z)
• CRAFT: Criticality-Aware Fault-Tolerance Enhancement Techniques for Emerging Memories-Based Deep Neural Networks [7.566423455230909]
  Deep Neural Networks (DNNs) have emerged as the most effective programming paradigm for computer vision and natural language processing applications. This paper proposes CRAFT, i.e., Criticality-Aware Fault-Tolerance Enhancement Techniques to enhance the reliability of NVM-based DNNs.
  arXiv  Detail & Related papers  (2023-02-08T03:39:11Z)
• 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)
• Leveraging Unlabeled Data to Predict Out-of-Distribution Performance [63.740181251997306]
  Real-world machine learning deployments are characterized by mismatches between the source (training) and target (test) distributions. In this work, we investigate methods for predicting the target domain accuracy using only labeled source data and unlabeled target data. We propose Average Thresholded Confidence (ATC), a practical method that learns a threshold on the model's confidence, predicting accuracy as the fraction of unlabeled examples.
  arXiv  Detail & Related papers  (2022-01-11T23:01:12Z)
• Shift-Robust GNNs: Overcoming the Limitations of Localized Graph Training data [52.771780951404565]
  Shift-Robust GNN (SR-GNN) is designed to account for distributional differences between biased training data and the graph's true inference distribution. We show that SR-GNN outperforms other GNN baselines by accuracy, eliminating at least (40%) of the negative effects introduced by biased training data.
  arXiv  Detail & Related papers  (2021-08-02T18:00:38Z)
• S2-BNN: Bridging the Gap Between Self-Supervised Real and 1-bit Neural Networks via Guided Distribution Calibration [74.5509794733707]
  We present a novel guided learning paradigm from real-valued to distill binary networks on the final prediction distribution. Our proposed method can boost the simple contrastive learning baseline by an absolute gain of 5.515% on BNNs. Our method achieves substantial improvement over the simple contrastive learning baseline, and is even comparable to many mainstream supervised BNN methods.
  arXiv  Detail & Related papers  (2021-02-17T18:59:28Z)

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.