Practical Compile Time Analyses for Evolving C Systems

在大型和不断发展的软件系统中， 编译器、测试器和调试器要求静态 信息得到维护和更新。 数据流分析 算法，收集有关数据使用和定义的事实 在节目中，是这些信息的主要来源。 增量更新算法，仅计算信息 受程序变化的影响，避免总的重新计算， 高效地为大型、 不断发展的软件系统。 在以前的研究中，实施增量 C系统的分析一直受到以下不精确性的阻碍： 用于确定指针引起的别名的已知技术。 本研究旨在开发更精确的跨过程 基于条件分析的C系统分析 我们的指针混淆近似算法的方法。 具体而言，计划： o优化过程间指针别名 近似算法， o开发一个新的跨过程修改方面 类C语言效果分析算法， 设计其他流量敏感数据流分析 (e.g.,过程间到达定义，静态 程序切片）， o建造第二个增量式MOD分析仪原型 并分析其在实际 C系统，以及 o研究扩大使用 大规模编程

In large and evolving software systems, tools such as compilers, testers, and debuggers require that static information be maintained and updated. Data flow analysis algorithms, which gather facts about use and definition of data in programs, are a major source of this information. Incremental update algorithms, which only calculate information affected by the program changes, avoiding total recalculation, efficiently provide consistent documentation for a large, evolving software system. In previous research, implementation of incremental analysis for C systems has been hampered by the imprecision of the known techniques for determining pointer-induced aliases. This research aims at developing more precise interprocedural analyses of C systems, based on the conditional analysis approach of our pointer aliasing approximation algorithm. Specifically, it is planned to: o optimize the interprocedural pointer aliasing approximation algorithm, o develop a new, interprocedural modification side effect analysis algorithm for C-like languages and design other flow sensitive data flow analyses (e.g., interprocedural reaching definitions, static program slicing), o build a second prototype incremental MOD analyzer for C systems and profile its performance on actual C systems, and o study issues involved in scaling up for programming-in-the-large.