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.

翻译：本研究提出了一种用于软件应用性能差距分析与建议的概念性框架PerfDetectiveAI。对于软件开发者而言，保持竞争优势并提供卓越用户体验的关键在于最大化应用性能。然而，由于确定性能差距并制定有效改进策略所涉及的复杂性，有必要探索创新性方法。PerfDetectiveAI利用现代机器学习（ML）与人工智能（AI）技术，监控性能指标并识别软件应用中的低效区域。该框架通过运用先进算法及复杂的数据分析方法，旨在协助软件开发者和性能工程师提升应用性能并提高系统生产力。基于AI、ML及软件工程领域的理论基础，PerfDetectiveAI构想了能够揭示细微性能差异并定位潜在性能瓶颈的精密系统。通过整合高级算法、统计建模与预测性分析，PerfDetectiveAI旨在为实践者提供数据驱动的建议以指导其决策流程。尽管PerfDetectiveAI目前仍处于概念阶段，本文阐述了该框架的核心原理、底层方法论及预期工作流程。我们希望通过提出这一概念性框架，激励更多关于AI驱动的性能优化领域的研究与开发工作，为追求卓越软件性能的后续发展奠定基础。