Classifying Code Comments with LLMs on the Cloud
Word count: 3500 words
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Objectives to cover:
Introduction – Introducing the importance of automated code comment classification in software engineering.
Objectives of the Study – Defining the study’s aim to classify code comments using LLMs and deep learning on cloud platforms.
Background and Motivation – Explaining the motivation behind using intelligent models for analyzing code comments.
Role of Large Language Models (LLMs) – Highlighting how LLMs understand and process natural language in code comments.
Deep Learning Techniques for Text Classification – Describing neural network methods used to classify and interpret text data.
Integration with Cloud Infrastructure – Discussing the deployment and scalability advantages of using cloud-based solutions.
System Architecture and Workflow – Outlining the design, data flow, and components of the proposed classification system.
Performance Metrics and Results – Presenting model evaluation metrics and results demonstrating system effectiveness.
Conclusion – Summarizing key findings and emphasizing the future potential of cloud-based LLM solutions in software analysis.
Reference: IEEE style