An Easy-to-Use Toolkit for Efficient Java Bytecode Translators

Chiba, Shigeru; Nishizawa, Muga

doi:10.1007/978-3-540-39815-8_22

Cited by 82 publications

(63 citation statements)

References 12 publications

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“…While the instrumentation process using ASM may in many cases be more efficient than using BCEL, we found that BCEL gives finer control on the generated code, and that its representation of method bodies is better suited for instrumentation at the basic block level. Javassist [14,15], which is used by JBoss [31], enables structural reflection and provides convenient source-level abstractions.…”

Section: Bytecode Manipulationmentioning

confidence: 99%

Platform‐independent profiling in a virtual execution environment

et al. 2008

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SUMMARYVirtual execution environments, such as the Java Virtual Machine, promote platform-independent software development. However, when it comes to analyzing algorithm complexity and performance bottlenecks, available tools focus on platform-specific metrics, such as e.g. the CPU time consumption on a particular system. Other drawbacks of many prevailing profiling tools are high overhead, significant measurement perturbation, as well as reduced portability of profiling tools, which are often implemented in platformdependent native code. This article presents a novel profiling approach, which is entirely based on program transformation techniques, in order to build a profiling data structure that provides calling-contextsensitive program execution statistics. We explore the use of platform-independent profiling metrics, in order to make the instrumentation entirely portable and to generate reproducible profiles. We implemented these ideas within a Java-based profiling tool called JP. A significant novelty is that this tool achieves complete bytecode coverage, by statically instrumenting the core runtime libraries, and dynamically instrumenting the rest of the code. JP provides a small and flexible API to write customized profiling agents in pure Java, which are periodically activated to process the collected profiling information. Performance measurements point out that, despite the presence of dynamic instrumentation, JP causes significantly less overhead than a prevailing tool for the profiling of Java code.

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Section: Bytecode Manipulationmentioning

confidence: 99%

Platform‐independent profiling in a virtual execution environment

et al. 2008

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“…In addition to transforming classes at load time with the Javassist library [9], our system includes a class differencing module and code generators for proxy, virtual superclass, and helper classes. The differencing algorithm operates at the bytecode level, and its output parameterizes the code generators and the bytecode rewriter.…”

Section: Flexible In-vivo Enhancement With Hotswapmentioning

confidence: 99%

Flexible and Efficient In-Vivo Enhancement for Grid Applications

Kim

Jiao

Tilevich

2009

2009 9th IEEE/ACM International Symposium on Cluster Computing and the Grid

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“…Although the applications are limited to Java programs, we have decided to use Javassist [9] to edit binary codes of Java programs. Javassist allows to change class definition, add fields and methods, change methods and so on without deep knowledge on the binary programs.…”

Section: Manual Operation Of Mobility/applicationmentioning

confidence: 99%