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Efficient online detection of dynamic control dependence
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International Symposium on Software Testing and Analysis archive
Proceedings of the 2007 international symposium on Software testing and analysis table of contents
London, United Kingdom
SESSION: Dynamic analysis table of contents
Pages: 185 - 195  
Year of Publication: 2007
ISBN:978-1-59593-734-6
Authors
Bin Xin  Purdue University
Xiangyu Zhang  Purdue University
Sponsors
ACM: Association for Computing Machinery
SIGSOFT: ACM Special Interest Group on Software Engineering
Publisher
ACM  New York, NY, USA
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Downloads (6 Weeks): 10,   Downloads (12 Months): 59,   Citation Count: 6
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ABSTRACT

Capturing dynamic control dependence is critical for many dynamic program analysis such as dynamic slicing, dynamic information flow, and data lineage computation. Existing algorithms are mostly a simple runtime translation of the static definition, which fails to capture certain dynamic properties by its nature, leading to inefficiency. In this paper, we propose a novel online detection technique for dynamic control dependence. The technique is based upon a new definition, which is equivalent to the existing one in the intraprocedural case but it enables an efficient detection algorithm. The new algorithm naturally and efficiently handles interprocedural dynamic control dependence even in presence of irregular control flow. Our evaluation shows that the detection algorithm slows down program execution by a factor of 2.57, which is 2.54 times faster than the existing algorithm that was used in prior work.


REFERENCES

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A. Beszedes, T. Gergely, and T. Gyimothy. Graphless dynamic dependence-based dynamic slicing algorithms. pages 21--30, Philadelphia, Pennsylvania, 2006.
 
5
Diablo binary rewriting framework. http://www.elis.ugent.be/diablo/.
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7
 
8
 
9
10
 
11
J. Newsome and D. Song. Dynamic taint analysis for automatic detection, analysis, and signature generation of exploits on commodity software. Technical Report CMU-CS-04-140, Carnegie Mellon University, 2004.
 
12
 
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V. Ranganath, T. Amtoft, A. Banerjee, M. Dwyer, and J. Hatcliff. A new foundation for control-dependence and slicing for modern program structures. In ESOP 2005: The European Symposium on Programming, pages 77--93, Edinburgh, Scotland, 2005.
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15
 
16
 
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M. Zhang, X. Zhang, X. Zhang, and S. Prabhakar. Tracing lineage beyond relational operators. Technical report, Purdue University, 2007.
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