Enabling Performance Antipatterns to Arise from an ADL-based Software Architecture

While the performance analysis of a software architecture is a quite well-assessed task nowadays, the issue of interpreting the performance results for providing feedback to software architects is still very critical. This is mostly due to the gap between results representation (i.e. mean values, variances, and/or probability distributions) and expected feedback (i.e. architectural alternatives). Performance antipatterns represent effective instruments to tackle this issue, because they document common mistakes leading to performance problems as well as their solutions. In this paper we present a model-driven approach that enables performance antipatterns to arise in the context of an ADL-based software architecture. Such approach automatically detects them in AEmilia, i.e. an ADL allowing the performance evaluation of software systems. The approach has been applied to a case study, and experimental results demonstrate its effectiveness.