Static Dalvik Bytecode Optimization for Android Applications

Abstract

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.

Is Your Android App Insecure? Patching Security Functions With Dynamic Policy Based on a Java Reflection Technique

“…The Android bytecode is Dalvik bytecode. This register-based instruction set allocates an available register range, depending on the number of variables and parameters declared for each method [17]. The register range is divided into 4 bits, 8 bits, and 16 bits [18].…”

Design an Optimized Compiler to Enhance Performance of Android Applications

“…ART supported multiple compilers based on AOTC (Ahead Of Time) compilation [2]. AOTCs are divided into two categories: standalone-mode and mixed-mode.…”