Interactive Debugging through Natural Language Inputs: Bridging the Gap between Human and Machine Understanding

Abstract

Interactive debugging through natural language inputs represents a transformative approach in software development, bridging the gap between human communication and machine understanding. This innovation addresses the challenges faced by novice programmers and non-technical stakeholders in traditional debugging environments. By integrating sophisticated Natural Language Processing models with debugging tools, the system enables users to describe programming issues in their native language while receiving contextually relevant solutions. The architecture encompasses input processing, context analysis, suggestion generation, and interactive feedback mechanisms, leading to enhanced debugging efficiency and improved developer productivity. Through empirical evaluations and user experience assessments, the implementation demonstrates significant improvements in query interpretation accuracy, response generation time, and overall debugging effectiveness. The system's impact extends beyond technical improvements, fostering better collaboration between technical and non-technical team members while making software development more accessible to diverse user groups.