Performance Antipattern Detection through fUML Model Library

Abstract

Identifying 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. Performance antipatterns support the interpretation of performance analysis results and help to fill the gap between numbers and design alternatives. In this paper, we present a model-driven framework that enables an early detection of performance antipatterns, i.e., without generating performance models. Specific design features (e.g., the number of sent messages) are monitored while simulating the specified software model, in order to point out the model elements that most likely contribute for performance flaws. To this end, we propose to use fUML models instrumented with a reusable library that provides data structures (as Classes) and algorithms (as Activities) to detect performance antipatterns while simulating the fUML model itself. A case study is provided to show our framework at work, its current capabilities and future challenges.