Comparison of Java and .NET reflection mechanisms for dynamic module loading: a performance benchmark study
Article Sidebar
Issue Vol. 40 (2026)
-
Analysis of the capabilities of predictive artificial intelligence models in corporate risk management
Kacper Ziemski188-192
-
Usability and availability of selected e-commerce services
Marcin Kozicki, Maria Skublewska-Paszkowska193-200
-
Comparison of C++ and Python performance based on selected algorithms
Szymon Bogucki, Kacper Burda201-205
-
Security analysis of selected web applications using vulnerability scanners
Mariusz Choroś, Marta Dziuba-Kozieł206-212
-
Comparison of Java and .NET reflection mechanisms for dynamic module loading: a performance benchmark study
Michał Mazur, Sebastian Maruszak, Marek Miłosz213-217
-
Comparative analysis of network vulnerability detection tools
Mateusz Zdunek218-225
-
Evaluation of mobile applications for personal finance management using the MARS scale
Łukasz Nikiel, Artsiom Patskevich, Marek Miłosz226-231
-
Comparison of the effectiveness of roulette betting strategies using Monte Carlo simulation
Marek Sarnecki232-238
-
Analysis of optimization capabilities of selected database management systems
Paweł Tarkiewicz, Małgorzata Plechawska-Wójcik239-246
-
Comparative analysis of Espresso and Appium frameworks for automated UI testing of Android mobile applications
Jakub Derkacz247-254
-
Comparative analysis of the applicability of artificial intelligence models for code generation
Patryk Warchoł, Małgorzata Plechawska-Wójcik255-262
-
Comparison of the effectiveness of selected tools for detecting texts generated by artificial intelligence
Marcin Brodacki, Małgorzata Plechawska-Wójcik263-269
-
Comparative analysis of selected containerization tools in terms of MCP
Paweł Jan Tłusty, Maciej Pańczyk270-276
-
SpikeCliff effect: empirical analysis of deterministic timing discontinuities in sponge-based XOF functions
Łukasz Wójcik, Stanisław Lota277-282
-
Comparison of AI agents for creating SQL queries
Julia Sierpień, Maria Skublewska-Paszkowska283-288
-
Comparative analysis of the performance of PostgreSQL and Neo4j databases in the context of genealogical queries
Michał Muzyka, Mateusz Niedźwiedź, Marek Miłosz289-296
-
Evaluation of the effectiveness of static and dynamic methods in malware analysis
Dominik Tracz, Daniel Sawicki, Konrad Gromaszek297-303
-
Comparison of classical machine learning methods in the task of obesity level classification
Paweł Biesaga, Paweł Powroźnik304-312
Main Article Content
Authors
Abstract
This paper presents an experimental performance analysis of dynamic module loading via reflection on the Java Virtual Machine (JVM) and the .NET Common Language Runtime (CLR). Three cold-loading benchmark scenarios are defined and executed with N=1000 post-warm-up iterations each: (A) Java URLClassLoader (non-modular JAR), (B) Java JPMS ModuleLayer (modular JAR), and (C) .NET AssemblyLoadContext. Each iteration measures four consecutive phases: module loading, type resolution, instance creation, and method invocation. After JIT warm-up (W=50 iterations) and IQR-based outlier removal, .NET AssemblyLoadContext achieves the lowest total mean time of 0.419 +- 0.104 ms, whereas Java JPMS incurs the highest mean of 1.299 +- 0.532 ms driven primarily by ModuleLayer construction (5.2x overhead vs. URLClassLoader). .NET type resolution is 20.9x faster than Java URLClassLoader (0.030 ms vs. 0.626 ms). Non-parametric statistical analysis (Mann–Whitney U, Cohen’s d: 0.66–2.26) confirms that all pairwise differences are highly significant (p<0.001). Cached-reflection baselines show that both platforms reduce to sub-10 us once class/assembly metadata is reused.
Keywords:
Sustainable Development Goal (SDG)
- Industry, Innovation, Technology and Infrastructure
References
[1] R. Johnson, Expert One-on-One J2EE Design and Development, Wrox Press, Birmingham, 2002.
[2] M. D. McIlroy, Mass Produced Software Components, in P. Naur, B. Randell (eds.), Software Engineering, Scientific Affairs Division, NATO, Brussels, 1969, pp. 138–155.
[3] OSGi Alliance, OSGi Core Release 8 Specification, https://docs.osgi.org/specification/, [03.04.2026].
[4] Oracle Corporation, The Reflection API - Java SE 21, https://docs.oracle.com/javase/tutorial/reflect/, [03.04.2026].
[5] Microsoft Corporation, Reflection in .NET, https://learn.microsoft.com/en-us/dotnet/fundamentals/reflection/overview, [03.04.2026].
[6] M. Reinhold, J. Steffan, JSR 376: Java Platform Module System, https://jcp.org/en/jsr/detail?id=376, [03.04.2026].
[7] Microsoft Corporation, AssemblyLoadContext Class - .NET 10, https://learn.microsoft.com/en-us/dotnet/api/system.runtime.loader.assemblyloadcontext?view=net-10.0, [03.04.2026].
[8] Microsoft Corporation, .NET Modularity and Plugins using AssemblyLoadContext, https://learn.microsoft.com/en-us/dotnet/core/tutorials/creating-app-with-plugin-support, [03.04.2026].
[9] I. Forman, N. Forman, Java Reflection in Action, Manning Publications, Greenwich, 2004.
[10] M. Pradel, P. Ratanaworabhan, T. R. Gross, Lean and Mean: Efficient Dynamic Dispatch in Prototype-Based Programs, Proceedings of ECOOP 7 (2011) 195-219.
[11] B. Fulgham, S. Nanz, The Computer Language Benchmarks Game, https://benchmarksgame-team.pages.debian.net/benchmarksgame/, [03.04.2026].
[12] M. Paleczny, C. A. Vick, C. Click, The Java HotSpot Server Compiler, Proceedings of the 1st Java Virtual Machine Research and Technology Symposium (JVM ’01), Monterey, CA, USA, April 23–24, 2001, USENIX Association, pp. 1–12.
[13] A. Georges, D. Buytaert, L. Eeckhout, Statistically Rigorous Java Performance Evaluation, ACM SIGPLAN Notices 42(10) (2007) 57-76.
[14] A. Shipilev, Java Microbenchmark Harness (JMH), GitHub repository, OpenJDK, https://github.com/openjdk/jmh, [03.04.2026].
[15] J. W. Tukey, Exploratory Data Analysis, Addison-Wesley, Reading, MA, 1977.
[16] H. B. Mann, D. R. Whitney, On a Test of Whether One of Two Random Variables is Stochastically Larger than the Other, The Annals of Mathematical Statistics 18(1) (1947) 50-60.
[17] J. Cohen, Statistical Power Analysis for the Behavioral Sciences, 2nd ed., Lawrence Erlbaum Associates, Hillsdale, NJ, 1988.
Article Details
Abstract views: 3

