Static program analysis is in general more precise if it is sensitive to execution contexts (execution paths). But then it is also more expensive in terms of memory consumption. For languages with conditions and iterations, the number of contexts grows exponentially with the program size. This problem is not just a theoretical issue. Several papers evaluating inter-procedural context-sensitive data-flow analysis report severe memory problems, and the path-explosion problem is a major issue in program verification and model checking.

In this paper we propose $\chi$-terms as a means to capture and manipulate context-sensitive program information in a data-flow analysis. $\chi$-terms are implemented as directed acyclic graphs without any redundant subgraphs.

To show the efficiency of our approach we run experiments comparing the memory usage of $\chi$-terms with four alternative data structures. Our experiments show that $\chi$-terms clearly outperform all the alternatives in terms of memory efficiency.

如果静态程序分析对执行上下文（执行路径）敏感，则通常更为精确。但是，就内存消耗而言，它也更加昂贵。对于具有条件和迭代的语言，上下文的数量随着程序的大小呈指数增长。这个问题不仅是理论上的问题。评估过程间上下文相关数据流分析的几篇论文报告了严重的内存问题，而路径爆炸问题是程序验证和模型检查中的主要问题。

在本文中，我们提出 $\chi$-术语，用于在数据流分析中捕获和操纵上下文相关的程序信息。 $\chi$项被实现为有向无环图，而没有任何冗余子图。

为了展示我们方法的效率，我们进行了实验，比较了内存​​的使用情况。 $\chi$-具有四个替代数据结构的术语。我们的实验表明$\chi$术语在内存效率方面显然胜过所有其他选择。