• The KIPS Transactions:PartD
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• v.15D no.2
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• pp.197-206
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• 2008

The CTOC framework was implemented to efficiently perform analysis and optimization of the Java bytecode that is often being used lately. In order to analyze and optimize the bytecode from the CTOC, the eCFG was first generated. Due to the bytecode characteristics of difficult analysis, the existing bytecode was expanded to be suitable for control flow analysis, and the control flow graph was drawn. We called eCFG(extended Control Flow Graph). Furthermore, the eCFG was converted into the SSA Form for a static analysis. Many loops were found in the conversion program. The previous CTOC performed conversion directly into the SSA Form without processing the loops. However, processing the loops prior to the SSA Form conversion allows more efficient generation of the SSA Form. This paper examines the process of finding the loops prior to converting the eCFG into the SSA Form in order to efficiently process the loops, and exhibits the procedures for generating the loop tree.

• Journal of KIISE:Software and Applications
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• v.32 no.12
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• pp.1283-1291
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• 2005

One of the problems in boosting Java performance using a Just-in-Time (JIT) compiler is removing redundant array bound checks. In conventional static compilers, many powerful algorithms have been developed, yet they are not directly applicable to JIT compilation where the compilation time is part of the whole running time. In the current JIT compilers, we tan use either a naive algorithm that is not powerful enough or an aggressive algorithm which requires the transformation into a static single assignment (SSA) form of programs (and back to the original form after optimization), thus causing too much overhead not appropriate for JIT compilation This paper proposes a new algorithm based on an inequality graph which can eliminate array bounds check codes aggressively without resorting to the SSA form. When we actually perform this type of optimization, there are many constraints in code motion caused by the precise exception rule in Java specification, which would cause the algorithm to miss many opportunities for eliminating away bound checks. We also propose a new method to overcome these constraints.

• Journal of KIISE:Software and Applications
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• v.31 no.10
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• pp.1304-1319
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• 2004

We can significantly reduce the cost o# program testing by automating the process of test data generation. Test data generation usually concerns identifying input values on which a selected path is executed. Although lots of research has been done so far, there still remains a lot of issues to be addressed. One of the issues is the shape problem. The shape problem refers to the problem of figuring out a shape of the input data structure required to cause the traversal of a given path. In this paper, we introduce a new method for the shape problem. The method converts the selected path into static single assignment (SSA) form without pointer dereferences. This allows us to consider each statement in the selected path as a constraint involving equality or inequality. We solve the constraints to get a solution which will be represented in terms of the points-to relations for each input variable. Simple, but illustrative examples are given to explain the proposed method.

• Journal of Digital Convergence
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• v.14 no.6
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• pp.253-261
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• 2016

As many people use internet through the various programs of PC and mobile devices, the possibility of private data leak is increasing. A program should be used after checking security of information flow. Security analysis of information flow is a method that analyzes security of information flow in program. If the information flow is secure, there is no leakage of personal information. If the information flow not secure, there may be a leakage of personal information. This paper proposes a method of analyzing information flow that facilitates SAT solver. The method translates a program that includes variables where security level is set into propositional formula representing control and information flow. The satisfiability of the formula translated is determined by using SAT solver. The security of program is represented through the result. Counter-example is generated if the program is not secure.