Towards detecting software performance anti-patterns using classification techniques

Abstract

This paper presents a non-intrusive machine learning approach called Non-intrusive Performance Anti-pattern Detecter (NiPAD) for identifying and classifying software performance anti-patterns. NiPAD uses only system performance metrics-as opposed to analyzing application level performance metrics or source code and the design of a software application to identify and classify software performance anti-patterns within an application. The results of applying NiPAD to an example application show that NiPAD is able to predict the One Lane Bridge software performance anti-pattern within a software application with 0.94 accuracy.