codeDEbug: AI Chatbot for All Coding Problems

The project “codeDEbug: AI Chatbot for All Coding Problems” successfully demonstrates the practical application of Artificial Intelligence in simplifying the debugging process in software development. Programming errors such as syntax issues, logical flaws, and runtime exceptions often create challenges for developers, especially beginners. The proposed system addresses these challenges by providing an intelligent, conversational platform that assists users in identifying and resolving coding problems efficiently. The chatbot, developed using Java and Spring Boot with integration of advanced AI through Gemini 2.5 Pro, is capable of understanding natural language queries as well as analyzing code across multiple programming languages. The system not only detects errors but also explains their root causes in simple and structured language, making it highly beneficial for learning and concept clarity. The results indicate that the chatbot significantly reduces debugging time, improves productivity, and enhances the overall programming experience. Unlike traditional debugging tools and online forums, the proposed system offers real-time, personalized assistance, eliminating the need for manual searching and interpretation. Its scalability, flexibility, and multi-language support make it suitable for both academic and professional environments. Although the system performs effectively, there is scope for further improvement. Future enhancements may include integration with Integrated Development Environments (IDEs), real-time code execution, and adaptive learning based on user behavior. In conclusion, the codeDEbug chatbot represents a step forward in AI-driven software development tools, highlighting how intelligent systems can make programming more accessible, efficient, and user-friendly.  

8.1. Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press, Cambridge, MA. Link: https://www.deeplearningbook.org This book provides fundamental concepts of deep learning and neural networks used in modern AI systems.   8.2.  Google AI. (2024). Gemini API Documentation. Link: https://ai.google.dev This documentation explains the architecture, usage, and integration of the Gemini 2.5 Pro model used in this project.   8.3.  Spring Framework Team. (2024). Spring AI Reference Documentation. Link: https://spring.io/projects/spring-ai This source provides complete information on integrating AI models into Spring Boot applications using Spring AI.   8.4.  Oracle Corporation. (2023). Java Platform, Standard Edition Documentation. Link: https://docs.oracle.com/javase This reference is used for Java programming concepts, APIs, and backend development practices. 8.5.  Gamma, E., Helm, R., Johnson, R., & Vlissides, J. (1994). Design Patterns: Elements of Reusable Object-Oriented Software. Addison-Wesley. Link: https://www.oreilly.com/library/view/design-patterns-elements/0201633612 This book provides object-oriented design principles used in backend system design.   8.6.  Sommerville, I. (2016). Software Engineering (10th Edition). Pearson Education. Link: https://www.pearson.com/en-us/subject-catalog/p/software-engineering/P200000003281 This book is referenced for software development lifecycle and system analysis.   8.7.  GitHub. (2024). GitHub Copilot Documentation. Link: https://docs.github.com/en/copilot This documentation is referenced for comparison with existing AI-based coding assistants.   8.8.  Stack Overflow. (2024). Developer Survey. Link: https://survey.stackoverflow.co This survey provides insights into common debugging challenges faced by developers worldwide.