Deep4ge：用于故障检测与诊断的深度神经网络训练轨迹
Deep4ge: DNN Training Trajectories for Fault Detection and Diagnosis
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中文总结 AI 辅助
研究针对深度学习系统因细微故障致训练行为改变的问题，提出Deep4ge基准数据集，通过59个适配程序生成14227次训练运行记录，含故障变体及相关特征，支持多种故障检测与诊断任务，并发布数据集与框架。
中文摘要 AI 辅助
深度学习系统常因细微的实现故障而失败，近期研究聚焦于从训练轮次的变化中检测和诊断此类故障。但软件工程领域缺乏带故障历史记录、特征提取细节及清晰复用支持的每轮训练运行的公共数据集。我们提出了Deep4ge，它是一个由从Stack Overflow收集的59个适配的TensorFlow/Keras深度神经网络程序生成的包含14227次训练运行的受控基准。通过27种源代码转换生成了有故障的变体，涵盖七类已知故障。数据集包含9845次有故障运行和4382次正确的基线运行。每次运行记录4个评估指标和26个特征。Deep4ge支持二进制故障检测、多类故障诊断以及从部分训练运行进行早期故障预测。我们在该https网址发布了数据集和故障注入框架。
英文摘要
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.
