• ETRI Journal
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• v.37 no.5
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• pp.1001-1011
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• 2015

Since just-in-time (JIT) has considerable overhead to detect hot spots and compile them at runtime, using sophisticated optimization techniques for embedded devices means that any resulting performance improvements will be limited. In this paper, we introduce a novel static Dalvik bytecode optimization framework, as a complementary compilation of the Dalvik virtual machine, to improve the performance of Android applications. Our system generates optimized Dalvik bytecodes by using Low Level Virtual Machine (LLVM). A major obstacle in using LLVM for optimizing Dalvik bytecodes is determining how to handle the high-level language features of the Dalvik bytecode in LLVM IR and how to optimize LLVM IR conforming to the language information of the Dalvik bytecode. To this end, we annotate the high-level language features of Dalvik bytecode to LLVM IR and successfully optimize Dalvik bytecodes through instruction selection processes. Our experimental results show that our system with JIT improves the performance of Android applications by up to 6.08 times, and surpasses JIT by up to 4.34 times.

• KIPS Transactions on Computer and Communication Systems
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• v.3 no.5
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• pp.151-154
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• 2014

An energy of applications had consumed through the complexed inter-action with operating systems, run-time environments, compiler, and applications on various mobile devices. In these days, challenged researches are studying to reduce of energy consumptions that uses energy-oriented high-level and low-level compiler techniques on mobile devices. In this paper, we intented to reduce an energy consumption of Java mobile applications that applied a list instruction scheduling for energy dissipation from dalvik bytecode which extracted Android dex files. Through this works, we can construct the optimized power and energy environment on mobile devices with the limited power supply.

• Journal of Software Engineering Society
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• v.28 no.1
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• pp.1-6
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• 2019

Google released an initial version of Android that runs Dex(Dalvik Executable) through the Dalvik Runtime. Since Dalvik Runtime is based on interpreter, JIT(Just-in-time) compilation has been applied to improve performance. After Lollipop(Android 5.0) Dalvik Runtime has replaced with ART Runtime which support AOT (Ahead-of-time) compilation of Dex into Native Code. The late st Android has a problem that the application execution speed is slow until the AOT compilation is completed according to the actual usage record after the installation of the app. To improve the problem we have investigate the characteristics of profile that can improve the execution speed of the application and generate the profile automatically. Finally we propose a method that can optimize the application at install time. With the proposed method we can optimize selectively at install time and can help improving the execution speed of the app from the initial execution.

• Proceedings of the Korea Information Processing Society Conference
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• 2012.04a
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• pp.101-102
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• 2012

안드로이드 플랫폼에 적합한 어플리케이션 보급이 급증하면서 안드로이드 가상머신인 달빅(dalvik)의 성능 향상을 위한 연구가 다양하게 시도되고 있다. 전력 공급이 제한적인 모바일 기기에서 효율적인 어플리케이션 실행을 위한 플랫폼의 성능 향상과 더불어 전력 에너지의 최적화된 소비가 중요한 이슈가 되고 있다. 이 논문은 달빅에서 실행되는 dex 파일의 바이트코드를 에너지 소비 중심으로 스케줄링하여 Java 어플리케이션의 전력 에너지 소비를 최적화하고자 하는 시도이다. 에너지 지향적인 스케줄링 기법은 전통적인 리스트-인스트럭션 스케줄링 기법을 기반으로 하였으며 스케줄링 전 후의 실험 결과를 제시하여 이 연구의 효과를 입증한다.

• The KIPS Transactions:PartA
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• v.19A no.4
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• pp.187-194
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• 2012

This paper proposes a string analysis based system for classifying Android Apps that may access so called harmful sites, and shows an experiment result for real Android apps on the market. The system first transforms Android App binary codes into Java byte codes, it performs string analysis to compute a set of strings at all program points, and it classifies the Android App as bad ones if the computed set contains URLs that are classified because the sites provide inappropriate contents. In the proposed approach, the system performs such a classification in the stage of distribution before installing and executing the Apps. Furthermore, the system is suitable for the automatic management of Android Apps in the market. The proposed system can be combined with the existing methods using DNS servers or monitoring modules to identify harmful Android apps better in different stages.