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Vol: 49(63) No: 3 / September 2004 Data Mining for Performance Antipatterns in Component Based Systems Using Run-Time and Static Analysis Trevor Parsons Performance Engineering Laboratory, Dublin City University, Ireland, , phone: (353)-1-7007644, e-mail: parsonst@eeng.dcu.ie John Murphy Performance Engineering Laboratory, University College Dublin, Ireland, , e-mail: JMurphy@ucd.ie Keywords: components, antipatterns, Data Mining, EJB, dynamic analysis Abstract Current complex distributed enterprise systems with performance requirements can be difficult to design for even the most experienced system designers. Shrinking development cycles exacerbate the problem as developers are often compelled to treat performance as an afterthought, a matter of production tuning to be accomplished after the system has been coded, integrated, functionally tested and deployed. Consequently enterprise systems often suffer from severe performance problems. We propose a framework for automatically detecting and assessing the impact of poor performance design (performance antipatterns) in component based systems, using a combination of run time and static analysis. The framework consists of four modules (a monitoring module, a detection module, an assessment module and a visualization module). The framework borrows techniques from the field of Knowledge Discovery in Databases. We intend to instantiate the framework for the Enterprise Java Bean platform. References [1] Smith, C. U. and Williams, L. G., “Performance Solutions: A Practical Guide to Creating Responsive, Scalable Software”, Addison-Wesley, Boston, MA, 2002. [2] Usama Fayyad, Gregory Piatetsky-Shapiro, and Padhraic Smyth. “The KDD process for extracting useful knowledge from volumes of data.” Communications of the ACM, volume 39, issue 11,1996. [3] Rakesh Agrawal, Tomasz Imielinski and Arun Swami. “Mining Association Rules between Sets of Items in Large Databases”. Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data, 1993. [4] http://www.quest.com/performasure [5] http://ejbperformance.org [6] Jochen Hipp, Ulrich Güntzer, Gholamreza Nakhaeizadeh “Algorithms for Association Rule Mining A General Survey and Comparison”. SIGKDD Explorations, 2000. [7] C. Kramer and L. Prechelt. “Design recovery by automated search for structural design patterns in object-oriented software”. Proceedings of the 3rd Working Conference on Reverse Engineering (WCRE), Monterey, CA, pages 208–215. November 1996. [8] Lothar Wendehals. “Improving Design Pattern Instance Recognition by Dynamic Analysis”. WODA, ICSE 2003 [9] Tate, B., Clark, M., Lee, B. and Linskey, P., “Bitter EJB”, Greenwich, CT, Manning, 2003. [10] Deepak Alur, John Crupi and Dan Malks. “Core J2EE Patterns: Best Practices and Design Strategies.” Prentice Hall / Sun Microsystems Press, 1st edition (June 26, 2001) |