A model-driven approach to catch performance antipatterns in ADL specifications

Context: 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. Performance antipatterns represent effective instruments to tackle this issue, because they document common mistakes leading to performance problems as well as their solutions. Objective: Up today performance antipatterns have been only studied in the context of software modeling languages like UML, whereas in this manuscript our objective is to catch them in the context of ADL-based software architectures to investigate their effectiveness. Method: We have implemented a model-driven approach that allows the automatic detection of four performance antipatterns in Æmilia, that is a stochastic process algebraic ADL for performance-aware component-oriented modeling of software systems. Results: We evaluate the approach by applying it to three case studies in different application domains. Experimental results demonstrate the effectiveness of our approach to support the performance improvement of ADL-based software architectures. Conclusion: We can conclude that the detection of performance antipatterns, from the earliest stages of software development, represents an effective instrument to tackle the issue of identifying flaws and improving system performance.