通过数据特性分析，提前发现机器学习代码中的语义错误。
Data-aware Static Analysis: Improving Detection of Semantic Faults in Machine Learning Code Using Data Characteristics
- 结合数据与控制流分析，识别模型训练中的语义缺陷。
- 在真实笔记本中验证，可发现需数据感知才能发现的错误。
- 适合机器学习开发者在编码阶段快速定位问题。
机器学习开发中特有的语义故障常导致预测性能不佳、计算开销过高或输出错误。例如，将未缩放的数据用于对尺度敏感的模型训练。当前开发者通常在模型训练后手动分析结果才察觉这些问题，效率低下。本文提出一种新的数据感知静态分析方法，可在编写代码阶段即检测此类语义错误。该方法融合数据流分析、控制流分析与API契约，实现对机器学习代码的高层抽象级数据感知推理。通过对真实世界机器学习笔记本样本的分析，验证了本方法能有效识别需数据感知才能发现的故障，具有实际应用潜力。
原文摘要 · Abstract (English)
Semantic faults specific to the use of machine learning models are a common problem for machine learning developers, causing suboptimal predictions, high computational cost, or incorrect outputs. For example, one may erroneously use unscaled data to train a scale-sensitive model. Machine learning developers detect these faults after training their models and manually analyzing the results, making it an inefficient process. We propose a novel data-aware static analysis approach to detect semantic faults in machine learning code, allowing developers to reveal these bugs while writing code instead of after training the model. Our approach uses combined data and control flow analysis, and API contracts, enabling data-aware reasoning about machine learning code at a high level of abstraction. We highlight the potential of our solution by analyzing a sample of real-world machine learning notebooks, finding that we can detect faults that require a data-aware approach.
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