Real-Time AI Powered StackOverflow

Abstract

This study proposes an intelligent AI-powered platform for software question-and-answer (Q&A) management, aimed at enhancing developer productivity and collaboration. Traditional platforms, such as Stack Overflow, often face slow responses, duplicate queries, and inconsistent quality of answers due to reliance on manual input. The proposed system leverages advanced AI models, including GPT and BERT, to automatically generate, validate, and rank answers in real-time, while detecting duplicate questions through semantic similarity analysis. Additionally, the system recommends bug fixes and improvements by analyzing submitted code snippets. Built using a Python Flask backend, React.js frontend with Tailwind CSS, and a MySQL/MongoDB database, the platform ensures a responsive, scalable, and user-friendly experience. By integrating AI-driven forecasting of trending technical queries, real-time feedback loops, and automated code validation, the system fosters efficient knowledge sharing and proactive developer assistance, addressing challenges of traditional Q&A platforms.

Introduction

Online developer communities like Stack Overflow are essential for knowledge sharing but face issues such as slow responses, duplicate questions, inconsistent coding practices, and outdated answers. Recent advances in AI, NLP, and deep learning enable automated, context-aware solutions to enhance these platforms. Transformer-based models (GPT, BERT) can generate answers, validate code, detect bugs, and provide best practices, improving efficiency and learning outcomes.

Proposed System:
An AI-powered Q&A platform integrates semantic search, automated answer generation, code validation, summarization, and contextual highlighting into a web-based interface. It leverages:

• Semantic Matching & Duplicate Detection: Identifies similar questions using embeddings to reduce redundancy.

• Answer Generation & Validation: GPT/BERT models generate context-aware responses and analyze code for errors and optimizations.

• Summarization & Contextual Highlighting: Condenses multi-answer threads, emphasizes key code snippets, and provides actionable insights.

• User Interaction: Real-time web interface allows voting, saving summaries, and dynamic discussion tracking.

Methodology & Architecture:

• Question Capture: React.js interface supports multi-line text, code snippets, and logs.

• Text Preprocessing: Tokenization, lemmatization, entity recognition, code detection, and noise filtering ensure accurate semantic understanding.

• Semantic Analysis & Ranking: Questions are converted into embeddings and compared to a database; answers are ranked based on similarity, recency, votes, and contextual relevance.

• System Optimization: Precomputed embeddings, caching, efficient indexing, asynchronous server operations, and resource management ensure fast, low-latency performance.

Results & Benefits:

• Real-time, accurate, contextually relevant answers.

• High-precision entity recognition for code, libraries, and errors.

• Concise summaries and actionable highlights reduce cognitive load.

• Decreased redundancy and faster problem-solving improve knowledge retention and collaboration.

Conclusion

This study demonstrates an AI-powered, context-aware Stack Overflow assistant that enhances developer productivity through real-time answer recommendations, entity recognition, and automated summarization. By integrating transformer-based NLP models with a MERN stack architecture, the system improves knowledge retrieval, reduces duplication, and highlights key information effectively. Future work includes: 1) Multilingual support for international developer communities 2) Improved code validation and execution environment integration 3) Enhanced IDE plugins for seamless workflow integration 4) Advanced learning from user interactions to improve recommendation accuracy The proposed system provides a scalable, intelligent solution for knowledge management in software development communities, bridging the gap between unstructured forum content and actionable insights.

References

[1] Vaswani, Ashish, et al. \"Attention is All You Need.\" NeurIPS, 2017. [2] Devlin, Jacob, et al. \"BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.\" NAACL, 2019. [3] Brown, Tom, et al. \"Language Models are Few-Shot Learners.\" NeurIPS, 2020. [4] Jiang, Mi, et al. \"NexaNota: An AI-Powered Smart Linked Lecture Note-Taking System Leveraging Large Language Models.\" ICBDE, 2025. [5] Wisoff, Josh, et al. \"NoteBar: An AI-Assisted Note-Taking System for Personal Knowledge Management.\" arXiv preprint, 2025. [6] Radford, Alec, et al. \"GPT-3: Language Models are Few-Shot Learners.\" OpenAI Technical Report, 2020. [7] Zhou, YunYu, et al. \"Extracting Learning Data From Handwritten Notes Using AI.\" IEEE Access, 2025.

Copyright

Copyright © 2025 T. P. Yagna Narayanan, S. Vishnu Priyan, S. Manikavasagam, Mrs. Mutheewari. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.