Structure-Sensitive Pointer Analysis for Multi-structure Objects

被引:0
|
作者
An, Xun [1 ]
Jia, Xiaoqi [2 ]
Du, Haichao [2 ]
Xie, YaMin [2 ]
机构
[1] Univ Chinese Acad Sci, Inst Informat Engn, Chinese Acad Sci, Beijing, Peoples R China
[2] Chinese Acad Sci, Inst Informat Engn, Beijing, Peoples R China
基金
中国国家自然科学基金;
关键词
Software analysis; Pointer analysis; Flow-sensitivity; Structure-sensitivity; Multi-structure; TO ANALYSIS;
D O I
10.1145/3671016.3671396
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Static analysis is a method within software analysis, and pointer analysis is an important component of static analysis. An important dimension of pointer analysis is field-sensitivity, which has been proven to effectively enhance the accuracy of pointer analysis results. A crucial area of research within field-sensitivity is structure-sensitivity. Structure-sensitivity has been shown to further enhance the precision of pointer analysis. However, existing structure-sensitive methods cannot handle cases where an object possesses multiple structures. This paper introduces MTO-SS, a multi-structure object pointer analysis method based on flow-sensitive pointer analysis. MTOSS incorporates the structural information of objects during flow-sensitive pointer analysis, making the pointer analysis results more accurate. Specifically, MTO-SS considers the structural information of an object when analyzing field access instructions to determine if the object possesses the corresponding field, in order to filter out spurious pointing relationships. To ensure the completeness of the object type set, MTO-SS always performs weak updates on the object's type set. This means that once an object acquires a structure, that structure will accompany the object throughout its entire lifecycle. We evaluated our multi-structure object pointer analysis method using the 12 largest programs from GNU Coreutils. Then, we compared the experimental results with the purely flowsensitive method, SPARSE. The results confirmed that MTO-SS is more accurate than SPARSE. Compared to SPARSE, the accuracy of MTO-SS improved by an average of 22.94%. Furthermore, the additional time and memory overhead introduced by MTO-SS is negligible.
引用
收藏
页码:155 / 164
页数:10
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