Abstract: Studies with a variety of viewpoints, goals, measurements, and quality characteristics have been conducted in order to determine the effect of design patterns on quality attributes. This has resulted in findings that are contradictory and difficult to compare. They want to explain these findings by taking into account confounding variables, practises, measurements, and implementation problems that have an impact on quality. Furthermore, there is a paucity of research that establishes a link between design pattern assessments and pattern creation studies, which is a significant limitation. For the purpose of detecting and categorising software performance anti-patterns, this article proposes a non-intrusive machine learning method dubbed Non-intrusive Performance Anti-pattern Detector (NiPAD). Keywords: software performance, anti-patterns, classification, machine learning, dynamic software analysis