加强分享分析技术：全面评价
Enhanced sharing analysis techniques: a comprehensive evaluation
摘要：
共享是D. Jacobs和A. Langen用于分析逻辑程序，导出有用的别名信息。众所周知，一个常用的核心技术，如共享与自由度和线性信息的集成，可以显着提高分析的精度。然而，许多其他关于细化域组合的建议已经流传多年。这些建议的一个共同特点是，它们似乎没有经过彻底的实验评估，即使是在预期的精度增益方面。在本文中，我们实验评估：帮助共享与明确的基础变量发现使用Pos，域的正布尔公式;明确的结构信息的合并;一个完整的实现减少产品的共享和Pos;重新排序的问题绑定在计算中的抽象MGU;增加一个新模式的原始建议，记录被认为是基础或自由的变量集;使用线性改进分析的改进方法;将共享与自由信息相结合，实现隐藏信息的恢复。最后，我们讨论的问题是否跟踪复合性允许计算更多的共享信息。
英文摘要：
Sharing, an abstract domain developed by D. Jacobs and A. Langen for the analysis of logic programs, derives useful aliasing information. It is well-known that a commonly used core of techniques, such as the integration of Sharing with freeness and linearity information, can significantly improve the precision of the analysis. However, a number of other proposals for refined domain combinations have been circulating for years. One feature that is common to these proposals is that they do not seem to have undergone a thorough experimental evaluation even with respect to the expected precision gains. In this paper we experimentally evaluate: helping Sharing with the definitely ground variables found using Pos, the domain of positive Boolean formulas; the incorporation of explicit structural information; a full implementation of the reduced product of Sharing and Pos; the issue of reordering the bindings in the computation of the abstract mgu; an original proposal for the addition of a new mode recording the set of variables that are deemed to be ground or free; a refined way of using linearity to improve the analysis; the recovery of hidden information in the combination of Sharing with freeness information. Finally, we discuss the issue of whether tracking compoundness allows the computation of more sharing information.
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。
