Performance Antipatterns: State-of-Art and Future Perspectives

摘要

The problem of capturing performance problems is critical in the software design, mostly because the results of performance analysis (i.e. mean values, variances, and probability distributions) are difficult to be interpreted for providing feedback to software designers. Support to the interpretation of performance analysis results that helps to fill the gap between numbers and design alternatives is still lacking. The aim of this talk is to present the work that has been done in the last few years on filling such gap. The work is centered on software performance antipatterns, that are recurring solutions to common mistakes (i.e. bad practices) affecting performance. Such antipatterns can play a key role in the software performance domain, since they can be used in the investigation of performance problems as well as in the formulation of solutions in terms of design alternatives.