Abstract—The increasing complexity of modern software sys- tems has rendered traditional debugging methods, such as manual code inspection and breakpoint analysis, progressively inefficient. This paper introduces Prompt-Oriented Code Under- standing (POCU), a novel paradigm that leverages Generative Artificial Intelligence (GenAI) to enable natural language-driven debugging directly within Integrated Development Environments (IDEs). We propose a system architecture that translates devel- oper queries in natural language into actionable code analysis and debugging operations. To facilitate continuous model improve- ment without compromising intellectual property, we introduce a Trust-Metric Federated Learning (TMFL) framework. TMFL allows the underlying GenAI model to be fine-tuned across disparate, private codebases (silos) while ensuring the integrity and accountability of contributions through a novel trust metric. Furthermore, acknowledging the critical need for transparency in AI-assisted tools, we present a framework to quantify and optimize the inherent trade-off between the explainability of the AI’s suggestions and its raw performance. We define metrics for both dimensions and formulate an optimization strategy to achieve a Pareto-optimal balance. Our conceptual framework and proposed methodologies lay the groundwork for a new generation of intelligent, intuitive, and trustworthy developer tools designed to significantly reduce debugging time and cognitive load. Index Terms—Generative AI, Code Understanding, Natural Language Processing, Software Debugging, Federated Learning, Explainable AI, IDE Integration
Mohan Siva Krishna Konakanchi (Tue,) studied this question.