Generic programming has found widespread application in object-oriented languages like Java. However, existing context-sensitive pointer analyses fail to leverage the benefits of generic programming. This paper introduces generic sensitivity , a new context customization scheme targeting generics. We design our context customization scheme in such a way that generic instantiation sites, i.e., locations instantiating generic classes/methods with concrete types, are always preserved as key context elements. This is realized by augmenting contexts with a type variable lookup map, which is efficiently generated in a context-sensitive manner throughout the analysis process. We have implemented various variants of generic-sensitive analysis in WALA and conducted extensive experiments to compare it with state-of-the-art approaches, including both traditional and selective context-sensitivity methods. The evaluation results demonstrate that generic sensitivity effectively enhances existing context-sensitivity approaches, striking a new balance between efficiency and precision. For instance, it enables a 1-object-sensitive analysis to achieve overall better precision compared to a 2-object-sensitive analysis, with an average speedup of 12.6 times (up to 62 times).

中文翻译：

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泛型敏感性：用于指针分析的泛型引导上下文敏感性


通用编程在 Java 等面向对象语言中得到了广泛的应用。然而，现有的上下文相关指针分析未能利用泛型编程的优势。本文介绍了泛型敏感性，一种针对泛型的新上下文定制方案。我们设计上下文定制方案的方式是通用实例化站点，即用具体类型实例化通用类/方法的位置，始终保留为关键上下文元素。这是通过使用类型变量查找映射来增强上下文来实现的，该映射在整个分析过程中以上下文敏感的方式有效生成。我们在 WALA 中实现了通用敏感分析的各种变体，并进行了广泛的实验，将其与最先进的方法进行比较，包括传统的和选择性的上下文敏感方法。评估结果表明，通用敏感性有效增强了现有的上下文敏感性方法，在效率和精度之间取得了新的平衡。例如，与 2 对象敏感分析相比，它使 1 对象敏感分析能够实现更高的整体精度，平均加速率为 12.6 倍（最高可达 62 倍）。