Improving Analysis and Visualizing of JVM Profiling Logs Using Process Mining
| Autor: | Neveen I. Ghali, O. M. Hassan, M. M. MohieEl Din, Mohamed M. S. Farag |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
Profiling (computer programming)
Computer science Adaptive optimization business.industry Programming language Process mining 020207 software engineering 02 engineering and technology computer.software_genre Software 0202 electrical engineering electronic engineering information engineering Information system 020201 artificial intelligence & image processing Pharmacology (medical) Program behavior Architecture Managed code Software engineering business computer |
| Zdroj: | Computer and Information Science. 9:54 |
| ISSN: | 1913-8997 1913-8989 |
| DOI: | 10.5539/cis.v9n1p54 |
| Popis: | Growing size and complexity of modern software applications increase the demand to make the information systems self-configuring, self-optimizing and with flexible architecture. Although managed languages have eliminated or minimized many low-level software errors there are many other sources of errors that persist. Java Virtual Machine (JVM), as managed language has many adaptive optimization techniques, which needs tools to analysis program behavior determines where the application spends most of its time. In this paper, new approached has been introduced to use process-mining techniques to represent the analysis and visualize phases of JVM profilers. They are flexible enough to cover so many perspectives in several ways. That can form a unified layer for analysis and visualize across profiling. |
| Databáze: | OpenAIRE |
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