A Process to Effectively Identify "Guilty" Performance Antipatterns

The problem of interpreting the results of software performance analysis is very critical. Software developers expect feedbacks in terms of architectural design alternatives (e.g., split a software component in two components and re-deploy one of them), whereas the results of performance analysis are either pure numbers (e.g. mean values) or functions (e.g. probability distributions). Support to the interpretation of such results that helps to fill the gap between numbers/functions and software alternatives is still lacking. Performance antipatterns can play a key role in the search of performance problems and in the formulation of their solutions. In this paper we tackle the problem of identifying, among a set of detected performance antipatterns, the ones that are the real causes of problems (i.e. the “guilty” ones). To this goal we introduce a process to elaborate the performance analysis results and to score performance requirements, model entities and performance antipatterns. The cross observation of such scores allows to classify the level of guiltiness of each antipattern. An example modeled in Palladio is provided to demonstrate the validity of our approach by comparing the performance improvements obtained after removal of differently scored antipatterns.

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