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作 者:QIAN Ju XU Baowen ZHOU Xiaoyu CHEN Lin SHI Liang
机构地区:[1]College of Information Science and Technology, NanjingUniversity of Aeronautics and Astronautics, Nanjing 210016,Jiangsu, China [2]State Key Laboratory for Novel Software Technology,Nanjing University, Nanjing 210093, Jiangsu, China [3]Department of Computer Science and Technology, NanjingUniversity, Nanjing 210093, Jiangsu, China [4]School of Computer Science and Engineering, SoutheastUniversity, Nanjing 211189, Jiangsu, China [5]Microsoft China Research and Development Group, Beijing100080, China
出 处:《Wuhan University Journal of Natural Sciences》2009年第4期321-326,共6页武汉大学学报(自然科学英文版)
基 金:Supported by the National High Technology Research and Development Program of China (863 Program) (2009AA01Z147);the National Natural Science Foundation of China (90818027, 60633010, 60803008);the National Science Foun for Distinguished Young Scholars (60425206)
摘 要:To avoid the precision loss caused by combining data- flow facts impossible to occur in the same execution path in dependence analysis for C programs, this paper first proposes a flow-sensitive and context-insensitive points-to analysis algorithm and then presents a new dependence analysis approach based on it. The approach makes more sufficient consideration on the executa- ble path problem and can avoid invalid combination between points-to relations and between points-to relations and reaching definitions. The results of which are therefore more precise than those of the ordinary dependence analysis approaches.To avoid the precision loss caused by combining data- flow facts impossible to occur in the same execution path in dependence analysis for C programs, this paper first proposes a flow-sensitive and context-insensitive points-to analysis algorithm and then presents a new dependence analysis approach based on it. The approach makes more sufficient consideration on the executa- ble path problem and can avoid invalid combination between points-to relations and between points-to relations and reaching definitions. The results of which are therefore more precise than those of the ordinary dependence analysis approaches.
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