| Project Overview | This project direction uses recent LLM-for-software-engineering research to improve software development tasks. The implementation can support code review, bug triage, testing, requirement extraction, documentation generation, or developer knowledge retrieval. The reference paper, 'A Survey of Large Language Models for Code: Evolution, Benchmarking, and Future Trends', provides the academic base for the topic. Instead of copying the paper abstract directly, this page keeps the same research intent in a safe paraphrased form: the system uses large-language-model capabilities for explanation, reasoning, summarization, and natural-language interaction. The final student implementation can include dataset preparation, model/API integration, dashboard or app interface, result explanation, and a short documentation-ready workflow. |
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| Research Paper Title | A Survey of Large Language Models for Code: Evolution, Benchmarking, and Future Trends |
| Research Paper / PDF Link | Open Paper / PDF |
| Year | 2023 |
| Project Area | Software Engineering with AI |
| Project Type | Software Project |
| Required Tools / Software | Python, GitHub API, LLM API, Streamlit/React, Static analysis tools, Vector DB |
| Main Features / Working Principle | Collect or upload relevant data, preprocess it, apply an AI/ML/LLM/RAG/software workflow, and present the result through a dashboard or application interface for Software Engineering with AI. |
| Expected Output | A working B.Tech project prototype for Software Engineering with AI with input, processing, result display, and explanation/report sections. |
| Possible Add-ons | Admin panel, PDF report export, model comparison, source citations, login system, WhatsApp help button, and deployment on cloud/hosting. |
| Get Help | Get Help on WhatsApp
Message: Hi FE, I need help with "Unit Test Generation System Using Code LLMs" in "Computer Science & Engineering" |
This B.Tech Computer Science & Engineering project resource connects a recent research direction with a practical implementation plan, tools, expected output, and possible extensions.