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Thematische Bibliographien / Java bytecode / Zeitschriftenartikel

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Zeitschriftenartikel zum Thema „Java bytecode“

Autor: Grafiati

Veröffentlicht am 28. Juni 2021

Zuletzt aktualisiert am 21. Juni 2025

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1

Gal, Andreas, Christian W. Probst, and Michael Franz. "Integrated Java Bytecode Verification." Electronic Notes in Theoretical Computer Science 131 (May 2005): 27–38. http://dx.doi.org/10.1016/j.entcs.2005.01.020.

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2

Kim, Ki-Tae, Je-Min Kim, and Weon-Hee Yoo. "Implementation of Java Bytecode Framework." Journal of the Korea Contents Association 10, no. 3 (2010): 122–31. http://dx.doi.org/10.5392/jkca.2010.10.3.122.

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3

Reynolds, Mark C. "Modeling the Java Bytecode Verifier." Science of Computer Programming 78, no. 3 (2013): 327–42. http://dx.doi.org/10.1016/j.scico.2011.03.008.

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4

Bertelsen, Peter. "Dynamic semantics of Java bytecode." Future Generation Computer Systems 16, no. 7 (2000): 841–50. http://dx.doi.org/10.1016/s0167-739x(99)00094-1.

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5

Cook, J. J. "Reverse Execution of Java Bytecode." Computer Journal 45, no. 6 (2002): 608–19. http://dx.doi.org/10.1093/comjnl/45.6.608.

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6

Zhao, Jian-jun. "Static analysis of Java bytecode." Wuhan University Journal of Natural Sciences 6, no. 1-2 (2001): 383–90. http://dx.doi.org/10.1007/bf03160273.

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7

Iqbal, Adeel, and Minhaj Ahmad Khan. "Optimizing Storage Space on Embedded Systems Using Superinstructions." STATISTICS, COMPUTING AND INTERDISCIPLINARY RESEARCH 6, no. 2 (2024): 219–36. https://doi.org/10.52700/scir.v6i2.164.

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Embedded systems are gaining popularity as the devices get smarter and customized with time. But still, embedded systems have limitations in their available resources such as storage and processing capability. This study investigates the role of superinstructions in optimizing the storage space of Java-based applications for embedded systems. JVM must also be optimized to meet embedded systems’ limited resources. This research introduces the JEOpt framework (Java Embedded Optimization framework) to systematically analyze Java bytecode, identify basic blocks, compute effectiveness of superinstructions, and subsequently modify the bytecode with superinstructions based on the effectiveness computed by the metric ESI. Java applications including xml, sunflow, scimark, mpegaudio, derby, crypto, compress, and compiler available in SPECjvm2008 benchmark were examined in this study. Prototype results show that the proposed approach achieves significant reductions in bytecode size up to 31.13% and effectiveness up to 32.3 for the Java applications executing on embedded systems.

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8

Dobravec, Tomaž. "JAVA BYTECODE INSTRUCTION USAGE COUNTING WITH ALGATOR." Acta Electrotechnica et Informatica 18, no. 4 (2018): 17–25. http://dx.doi.org/10.15546/aeei-2018-0028.

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9

Wang, Tao, and Abhik Roychoudhury. "Dynamic slicing on Java bytecode traces." ACM Transactions on Programming Languages and Systems 30, no. 2 (2008): 1–49. http://dx.doi.org/10.1145/1330017.1330021.

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10

Chan, Jien-Tsai, and Wuu Yang. "Advanced obfuscation techniques for Java bytecode." Journal of Systems and Software 71, no. 1-2 (2004): 1–10. http://dx.doi.org/10.1016/s0164-1212(02)00066-3.

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11

Ghosh, Sudipto, and John L. Kelly. "Bytecode fault injection for Java software." Journal of Systems and Software 81, no. 11 (2008): 2034–43. http://dx.doi.org/10.1016/j.jss.2008.02.047.

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12

Chrząszcz, Jacek, Patryk Czarnik, and Aleksy Schubert. "A Dozen Instructions Make Java Bytecode." Electronic Notes in Theoretical Computer Science 264, no. 4 (2011): 19–34. http://dx.doi.org/10.1016/j.entcs.2011.02.003.

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13

Leroy, Xavier. "Bytecode verification on Java smart cards." Software: Practice and Experience 32, no. 4 (2002): 319–40. http://dx.doi.org/10.1002/spe.438.

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14

Eichberg, Michael. "BAT2XML: XML-based Java Bytecode Representation." Electronic Notes in Theoretical Computer Science 141, no. 1 (2005): 93–107. http://dx.doi.org/10.1016/j.entcs.2005.02.035.

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15

Li, Zhiming, Qing Wu, and Kun Qian. "Adabot: Fault-Tolerant Java Decompiler (Student Abstract)." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 10 (2020): 13861–62. http://dx.doi.org/10.1609/aaai.v34i10.7203.

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Reverse Engineering has been an extremely important field in software engineering, it helps us to better understand and analyze the internal architecture and interrealtions of executables. Classical Java reverse engineering task includes disassembly and decompilation. Traditional Abstract Syntax Tree (AST) based disassemblers and decompilers are strictly rule defined and thus highly fault intolerant when bytecode obfuscation were introduced for safety concern. In this work, we view decompilation as a statistical machine translation task and propose a decompilation framework which is fully based on self-attention mechanism. Through better adaption to the linguistic uniqueness of bytecode, our model fully outperforms rule-based models and previous works based on recurrence mechanism.

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16

Jiang, Long Long, and Dai Ping Li. "Using Contour Marking Bytecode Verification Algorithm on the Java Card." Applied Mechanics and Materials 556-562 (May 2014): 4120–23. http://dx.doi.org/10.4028/www.scientific.net/amm.556-562.4120.

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Java bytecode verification could not be well performed in the smart card due to the resource usage especially in the resource-constrained devices. Currently, on the card there are several bytecode verifications which exist kinds of problems, in order to be better and be better adapt to the environment, such as a smart card platform, raised using the contour subroutine labeled bytecode verification algorithm on a card. First, through the analysis of existing card byte code verification algorithm to determine the imperfections and difficulties in the judgment and the verification of subroutine, and then propose a method for marking the subroutine in the place of the jump to it. Thus not only get the program structure and enhance the effectiveness and efficiency of the validation. The feasibility of the method is demonstrated by simulating typical examples verification.

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17

Ogata, Kazunori, Hideaki Komatsu, and Toshio Nakatani. "Bytecode fetch optimization for a Java interpreter." ACM SIGARCH Computer Architecture News 30, no. 5 (2002): 58–67. http://dx.doi.org/10.1145/635506.605404.

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18

Ogata, Kazunori, Hideaki Komatsu, and Toshio Nakatani. "Bytecode fetch optimization for a Java interpreter." ACM SIGOPS Operating Systems Review 36, no. 5 (2002): 58–67. http://dx.doi.org/10.1145/635508.605404.

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19

Munsil, Wes, and Chia-Jiu Wang. "Reducing stack usage in Java bytecode execution." ACM SIGARCH Computer Architecture News 26, no. 1 (1998): 7–11. http://dx.doi.org/10.1145/1216461.1216464.

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20

Qian, Zhenyu. "Standard fixpoint iteration for Java bytecode verification." ACM Transactions on Programming Languages and Systems 22, no. 4 (2000): 638–72. http://dx.doi.org/10.1145/363911.363915.

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21

Doyon, S., and M. Debbabi. "On object initialization in the Java bytecode." Computer Communications 23, no. 17 (2000): 1594–605. http://dx.doi.org/10.1016/s0140-3664(00)00245-0.

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22

Ogata, Kazunori, Hideaki Komatsu, and Toshio Nakatani. "Bytecode fetch optimization for a Java interpreter." ACM SIGPLAN Notices 37, no. 10 (2002): 58–67. http://dx.doi.org/10.1145/605432.605404.

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23

Avvenuti, Marco, Cinzia Bernardeschi, and Nicoletta De Francesco. "Java bytecode verification for secure information flow." ACM SIGPLAN Notices 38, no. 12 (2003): 20–27. http://dx.doi.org/10.1145/966051.966055.

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24

Stata, Raymie, and Martin Abadi. "A type system for Java bytecode subroutines." ACM Transactions on Programming Languages and Systems 21, no. 1 (1999): 90–137. http://dx.doi.org/10.1145/314602.314606.

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25

Vincenzi, A. M. R., M. E. Delamaro, J. C. Maldonado, and W. E. Wong. "Establishing structural testing criteria for Java bytecode." Software: Practice and Experience 36, no. 14 (2006): 1513–41. http://dx.doi.org/10.1002/spe.726.

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26

Collberg, Christian, Ginger Myles, and Michael Stepp. "An empirical study of Java bytecode programs." Software: Practice and Experience 37, no. 6 (2007): 581–641. http://dx.doi.org/10.1002/spe.776.

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27

Meehan, Gary, and Mike Joy. "Compiling lazy functional programs to Java bytecode." Software: Practice and Experience 29, no. 7 (1999): 617–45. http://dx.doi.org/10.1002/(sici)1097-024x(199906)29:7<617::aid-spe250>3.0.co;2-e.

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28

Albert, E., P. Arenas, S. Genaim, G. Puebla, and D. Zanardini. "Experiments in Cost Analysis of Java Bytecode." Electronic Notes in Theoretical Computer Science 190, no. 1 (2007): 67–83. http://dx.doi.org/10.1016/j.entcs.2007.02.061.

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29

Bernardeschi, C., N. De Francesco, G. Lettieri, and L. Martini. "Checking secure information flow in Java bytecode by code transformation and standard bytecode verification." Software: Practice and Experience 34, no. 13 (2004): 1225–55. http://dx.doi.org/10.1002/spe.611.

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30

Sherman, Elena, Yannic Noller, Cyrille Artho, et al. "The Java Pathfinder Workshop 2022." ACM SIGSOFT Software Engineering Notes 48, no. 1 (2023): 19–21. http://dx.doi.org/10.1145/3573074.3573080.

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Java Pathfinder (JPF) was originally developed as an explicit state software model checker and subsequently evolved into an extensible Java bytecode analysis framework that has been successfully used to implement techniques such as symbolic and concolic execution, compositional verification, parallel execution, incremental program analysis, and many more. Apart from its original domain of concurrent Java programs, JPF now supports the verification of new domains such as UMLs, numeric programs, graphical user interfaces, and Android applications.

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31

Haase, Oliver. "Abstract Interpretation of Java Bytecode for Immutability Analysis." Journal of Computer Science 12, no. 7 (2016): 314–22. http://dx.doi.org/10.3844/jcssp.2016.314.322.

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32

Clausen, Lars Ræder, Ulrik Pagh Schultz, Charles Consel, and Gilles Muller. "Java bytecode compression for low-end embedded systems." ACM Transactions on Programming Languages and Systems 22, no. 3 (2000): 471–89. http://dx.doi.org/10.1145/353926.353933.

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33

Jipping, Michael J., Cameron Calka, Brian O'Neill, and Christopher R. Padilla. "Teaching students java bytecode using lego mindstorms robots." ACM SIGCSE Bulletin 39, no. 1 (2007): 170–74. http://dx.doi.org/10.1145/1227504.1227371.

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34

Gal, Andreas, Christian W. Probst, and Michael Franz. "Java bytecode verification via static single assignment form." ACM Transactions on Programming Languages and Systems 30, no. 4 (2008): 1–21. http://dx.doi.org/10.1145/1377492.1377496.

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35

BARTHE, GILLES, DAVID PICHARDIE, and TAMARA REZK. "A certified lightweight non-interference Java bytecode verifier." Mathematical Structures in Computer Science 23, no. 5 (2013): 1032–81. http://dx.doi.org/10.1017/s0960129512000850.

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Non-interference guarantees the absence of illicit information flow throughout program execution. It can be enforced by appropriate information flow type systems. Much of the previous work on type systems for non-interference has focused on calculi or high-level programming languages, and existing type systems for low-level languages typically omit objects, exceptions and method calls. We define an information flow type system for a sequential JVM-like language that includes all these programming features, and we prove, in the Coq proof assistant, that it guarantees non-interference. An additional benefit of the formalisation is that we have extracted from our proof a certified lightweight bytecode verifier for information flow. Our work provides, to the best of our knowledge, the first sound and certified information flow type system for such an expressive fragment of the JVM.

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36

Payet, Étienne, and Fausto Spoto. "Experiments with Non-Termination Analysis for Java Bytecode." Electronic Notes in Theoretical Computer Science 253, no. 5 (2009): 83–96. http://dx.doi.org/10.1016/j.entcs.2009.11.016.

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37

Spoto, Fausto, and Étienne Payet. "Magic-sets for localised analysis of Java bytecode." Higher-Order and Symbolic Computation 23, no. 1 (2010): 29–86. http://dx.doi.org/10.1007/s10990-010-9063-7.

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38

McGhan, H., and M. O'Connor. "PicoJava: a direct execution engine for Java bytecode." Computer 31, no. 10 (1998): 22–30. http://dx.doi.org/10.1109/2.722273.

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39

Knoblock, Todd B., and Jakob Rehof. "Type elaboration and subtype completion for Java bytecode." ACM Transactions on Programming Languages and Systems 23, no. 2 (2001): 243–72. http://dx.doi.org/10.1145/383043.383045.

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40

Coglio, Alessandro. "Improving the official specification of Java bytecode verification." Concurrency and Computation: Practice and Experience 15, no. 2 (2003): 155–79. http://dx.doi.org/10.1002/cpe.714.

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41

Coglio, Alessandro. "Simple verification technique for complex Java bytecode subroutines." Concurrency and Computation: Practice and Experience 16, no. 7 (2004): 647–70. http://dx.doi.org/10.1002/cpe.798.

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42

Clausen, Lars R. "A Java bytecode optimizer using side-effect analysis." Concurrency: Practice and Experience 9, no. 11 (1997): 1031–45. http://dx.doi.org/10.1002/(sici)1096-9128(199711)9:11<1031::aid-cpe354>3.0.co;2-o.

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43

Bernardeschi, Cinzia, Giuseppe Lettieri, Luca Martini, and Paolo Masci. "A Space-Aware Bytecode Verifier for Java Cards." Electronic Notes in Theoretical Computer Science 141, no. 1 (2005): 237–54. http://dx.doi.org/10.1016/j.entcs.2005.02.027.

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44

Binder, Walter, and Jarle Hulaas. "Java Bytecode Transformations for Efficient, Portable CPU Accounting." Electronic Notes in Theoretical Computer Science 141, no. 1 (2005): 53–73. http://dx.doi.org/10.1016/j.entcs.2005.02.037.

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45

Lambert, Jonathan, Rosemary Monahan, and Kevin Casey. "Accidental Choices—How JVM Choice and Associated Build Tools Affect Interpreter Performance." Computers 11, no. 6 (2022): 96. http://dx.doi.org/10.3390/computers11060096.

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Considering the large number of optimisation techniques that have been integrated into the design of the Java Virtual Machine (JVM) over the last three decades, the Java interpreter continues to persist as a significant bottleneck in the performance of bytecode execution. This paper examines the relationship between Java Runtime Environment (JRE) performance concerning the interpreted execution of Java bytecode and the effect modern compiler selection and integration within the JRE build toolchain has on that performance. We undertook this evaluation relative to a contemporary benchmark suite of application workloads, the Renaissance Benchmark Suite. Our results show that the choice of GNU GCC compiler version used within the JRE build toolchain statistically significantly affects runtime performance. More importantly, not all OpenJDK releases and JRE JVM interpreters are equal. Our results show that OpenJDK JVM interpreter performance is associated with benchmark workload. In addition, in some cases, rolling back to an earlier OpenJDK version and using a more recent GNU GCC compiler within the build toolchain of the JRE can significantly positively impact JRE performance.

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46

GAMESS, ERIC. "EXECUTION OF SEQUENTIAL AND PARALLEL JAVA BYTECODE IN A METACOMPUTING SYSTEM." Parallel Processing Letters 13, no. 01 (2003): 53–64. http://dx.doi.org/10.1142/s0129626403001148.

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In this paper, we address the goal of executing Java parallel applications in a group of nodes of a Beowulf cluster transparently chosen by a metacomputing system oriented to efficient execution of Java bytecode, with support for scientific computing. To this end, we extend the Java virtual machine by providing a message passing interface and quick access to distributed high performance resources. Also, we introduce the execution of parallel linear algebra methods for large objects from sequential Java applications by invoking SPLAM, our parallel linear algebra package.

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47

Vartanov, S. P., and M. K. Ermakov. "Applying Java bytecode static instrumentation for software dynamic analysis." Proceedings of the Institute for System Programming of the RAS 27, no. 1 (2015): 25–38. http://dx.doi.org/10.15514/ispras-2015-27(1)-2.

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48

Papadimitriou, Stergios, Seferina Mavroudi, Kostas Theofilatos, and Spiridon Likothanasis. "MATLAB-Like Scripting of Java Scientific Libraries in ScalaLab." Scientific Programming 22, no. 3 (2014): 187–99. http://dx.doi.org/10.1155/2014/570902.

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Although there are a lot of robust and effective scientific libraries in Java, the utilization of these libraries in pure Java is difficult and cumbersome, especially for the average scientist that does not expertise in software development. We illustrate that ScalaLab presents an easier and productive MATLAB like front end. Also, the main strengths and weaknesses of the core Java libraries of ScalaLab are elaborated. Since performance is of paramount importance for scientific computation, the article discusses extensively performance aspects of the ScalaLab environment. Also, Java bytecode performance is compared to native code.

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49

Bockisch, Christoph, Gabriele Taentzer, Nebras Nassar, and Lukas Wydra. "Java Bytecode Verification with OCL Why, How and Whenc." Journal of Object Technology 19, no. 3 (2020): 3:1. http://dx.doi.org/10.5381/jot.2020.19.3.a13.

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50

Achour, Safaa, Ali Chouenyib, and Mohammed Benattou. "A Constraint-Based Verification Approach for Java Bytecode Programs." International Journal of Software Engineering and Its Applications 12, no. 2 (2018): 1–17. http://dx.doi.org/10.14257/ijseia.2018.12.2.01.

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