程序需要什么：输入数据规格的自动推断
What Programs Want: Automatic Inference of Input Data Specifications
摘要：
如今，随着机器学习软件迅速渗透到我们的社会中，我们越来越容易受到数据预处理或训练软件中的编程错误以及数据本身错误的影响。在本文中，我们提出了一个静态形状分析框架的输入数据的数据处理程序。我们的分析会自动推断数据处理程序读取的数据的结构和值的必要条件。我们的框架建立在一个家庭的底层抽象域，扩展到间接的原因，而不是简单的推理程序变量的输入数据。这些抽象域的选择是分析的参数。我们描述了从现有的抽象域构建的各种实例。所提出的方法是在一个开源的Python程序的静态分析器。我们展示了其潜力的一些代表性的例子。
英文摘要：
Nowadays, as machine-learned software quickly permeates our society, we are becoming increasingly vulnerable to programming errors in the data pre-processing or training software, as well as errors in the data itself. In this paper, we propose a static shape analysis framework for input data of data-processing programs. Our analysis automatically infers necessary conditions on the structure and values of the data read by a data-processing program. Our framework builds on a family of underlying abstract domains, extended to indirectly reason about the input data rather than simply reasoning about the program variables. The choice of these abstract domain is a parameter of the analysis. We describe various instances built from existing abstract domains. The proposed approach is implemented in an open-source static analyzer for Python programs. We demonstrate its potential on a number of representative examples.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
