语义保持变换下神经程序分析器的泛化能力评价
Evaluation of Generalizability of Neural Program Analyzers under Semantic-Preserving Transformations
摘要：
丰富的公开源代码库，结合神经网络的进步，使数据驱动的方法程序分析成为可能。这些方法称为神经程序分析器，使用神经网络来提取程序中的模式，用于从开发生产力到程序推理的任务。尽管神经程序分析器越来越受欢迎，但其结果的可推广程度尚不清楚。在本文中，我们使用七个语义等价的程序变换对两个流行的神经程序分析器的泛化能力进行了大规模的评估。我们的研究结果警告说，在许多情况下，神经程序分析器不能很好地概括，有时程序的文本差异可以忽略不计。这些结果为神经程序分析器中的鲁棒性量化提供了初步的基础。
英文摘要：
The abundance of publicly available source code repositories, in conjunction with the advances in neural networks, has enabled data-driven approaches to program analysis. These approaches, called neural program analyzers, use neural networks to extract patterns in the programs for tasks ranging from development productivity to program reasoning. Despite the growing popularity of neural program analyzers, the extent to which their results are generalizable is unknown. In this paper, we perform a large-scale evaluation of the generalizability of two popular neural program analyzers using seven semantically-equivalent transformations of programs. Our results caution that in many cases the neural program analyzers fail to generalize well, sometimes to programs with negligible textual differences. The results provide the initial stepping stones for quantifying robustness in neural program analyzers.
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。
