JEETuningExpert: A software assistant for improving Java Enterprise Edition application performance

  • Authors:

  • Marco Crasso;Alejandro Zunino;Leonardo Moreno;Marcelo Campo

  • Affiliations:

  • ISISTAN Research Institute, UNICEN University, Campus Universitario, Tandil (B7001BBO), Buenos Aires, Argentina;ISISTAN Research Institute, UNICEN University, Campus Universitario, Tandil (B7001BBO), Buenos Aires, Argentina;ISISTAN Research Institute, UNICEN University, Campus Universitario, Tandil (B7001BBO), Buenos Aires, Argentina;ISISTAN Research Institute, UNICEN University, Campus Universitario, Tandil (B7001BBO), Buenos Aires, Argentina

  • Venue:
  • Expert Systems with Applications: An International Journal
  • Year:
  • 2009

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Abstract

Designing a JEE (Java Enterprise Edition)-based enterprise application capable of achieving its performance objectives is rather hard. Predicting the performance of this type of systems at the design level is difficult and sometimes not viable, because this requires having precise knowledge of the expected load conditions and the underlying software infrastructure. Besides, the requirement for rapid time-to-market leads to postpone performance tuning until systems are developed, packaged and running. In this paper we present a novel approach for automatically detecting performance problems in JEE-based applications and, in turn, suggesting courses of actions to correct them. The idea is to allow developers to smoothly identify and eradicate performance anti-patterns by automatically analyzing execution traces. The approach has been implemented as a tool called JEETuningExpert, and validated using three well-known JEE reference applications. Specifically, we evaluated the effectiveness of JEETuningExpert for detecting performance problems, measured the overhead imposed by online monitoring each application and the improvements were achieved after following the suggested corrective actions. These results empirically showed that the refactored applications are 40.08%, 76.94% and 61.13% faster, on average.