Rule-based automatic software performance diagnosis and improvement

  • Authors:

  • Jing Xu

  • Affiliations:

  • System and Computer Engineering, Carleton University, Ottawa, Ontario, Canada

  • Venue:
  • Performance Evaluation
  • Year:
  • 2010

Quantified Score

Hi-index 0.00

Visualization

Abstract

There are many advantages to analyzing performance at the design level, rather than waiting until system testing. However the necessary expertise in making and interpreting performance models may not be available, and the time for the analysis may be prohibitive. This work addresses both these difficulties through automation. Starting from an annotated specification in UML, it is possible to automatically derive a performance model. This work goes further to automate the performance analysis, and to explore design changes using diagnostic and design-change rules. The rules generate improved performance models which can be transformed back to an improved design. They untangle the effects of the system configuration (such as the allocation of processors) from limitations of the design, and they recommend both configuration and design improvements. This paper describes a prototype called Performance Booster (PB), which incorporates several rules, and demonstrates its feasibility by applying PB to the design of several case studies (tutorial and industrial). It also addresses how the changes at the level of a performance model should be implemented in the software.