  PerfDetectiveAI, a conceptual framework for performance gap analysis and
suggestion in software applications is introduced in this research. For
software developers, retaining a competitive edge and providing exceptional
user experiences depend on maximizing application speed. But investigating
cutting-edge approaches is necessary due to the complexity involved in
determining performance gaps and creating efficient improvement tactics. Modern
machine learning (ML) and artificial intelligence (AI) techniques are used in
PerfDetectiveAI to monitor performance measurements and identify areas of
underperformance in software applications. With the help of the framework,
software developers and performance engineers should be able to enhance
application performance and raise system productivity. It does this by
utilizing sophisticated algorithms and utilizing sophisticated data analysis
methodologies. Drawing on theoretical foundations from the fields of AI, ML and
software engineering, PerfDetectiveAI envisions a sophisticated system capable
of uncovering subtle performance discrepancies and identifying potential
bottlenecks. PerfDetectiveAI aims to provide practitioners with data-driven
recommendations to guide their decision-making processes by integrating
advanced algorithms, statistical modelling, and predictive analytics. While
PerfDetectiveAI is currently at the conceptual stage, this paper outlines the
framework's fundamental principles, underlying methodologies and envisioned
workflow. We want to encourage more research and development in the area of
AI-driven performance optimization by introducing this conceptual framework,
setting the foundation for the next developments in the quest for software
excellence.
