构建1.4万次DNN训练轨迹数据集，用于故障检测与诊断研究。
Deep4ge: DNN Training Trajectories for Fault Detection and Diagnosis
- 通过27种代码变换生成带故障的DNN训练运行数据
- 包含9845个故障样本与4382个正常基线样本，每轮记录26个行为特征
- 支持早期故障预警和多类别故障诊断，适合软件工程与AI可靠性研究
深度学习系统常因细微实现错误导致失败，这些错误会改变训练行为。现有研究虽尝试从训练周期变化中检测与诊断故障，但软件工程领域仍缺乏公开的、带有故障历史记录、特征提取细节且便于复用的每轮训练数据集。本文提出Deep4ge，一个由59个源自Stack Overflow的适配TensorFlow/Keras DNN程序生成的受控基准数据集，共14,227次训练运行。通过27种源码变换引入七类已知故障，生成9,845个故障运行和4,382个正确基线运行。每次运行记录4项评估指标和26个特征，涵盖权重、梯度、激活值、准确率与损失趋势、学习率及硬件使用情况。该数据集支持二元故障检测、多类别故障诊断以及从部分训练过程中的早期故障预测。数据集与故障注入框架已公开于https://doi.org/10.5281/zenodo.20337241。
原文摘要 · Abstract (English)
Deep learning systems often fail due to subtle implementation faults that alter training behavior. Recent work has studied how to detect and diagnose such failures from changes observed across training epochs. However, the software engineering community still lacks a public dataset of per-epoch training runs with documented fault history, feature extraction details, and clear reuse support for fault detection and diagnosis tasks. We present Deep4ge, a controlled benchmark of 14,227 training runs generated from 59 adapted TensorFlow/Keras deep neural network (DNN) programs collected from Stack Overflow. We generated faulty variants using 27 source-code transformations that introduce known faults across seven categories. The dataset contains 9,845 faulty runs and 4,382 correct baseline runs. For each run, we record 4 evaluation metrics and 26 features that measure training behavior at every epoch. These features capture weights, gradients, activations, accuracy and loss trends, learning rate, and hardware use. Deep4ge supports binary fault detection, multi-class fault diagnosis, and early fault prediction from partial training runs. We release the dataset and fault-injection framework at https://doi.org/10.5281/zenodo.20337241.
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