Enhancing Developer Productivity: Benchmarking LLM-Powered Tools like GitHub Copilot and TabNine in Real-Time Coding Environments

The adoption of Large Language Models (LLMs) is disrupting software development — including code generation, debugging, and project management. We classify LLM applications into six domains specializing in general usage, templated interactive development environments (IDEs) programming, complex reasoning, cloud programming, large-volume code management, and bilingual project programming. It assesses models including GPT-4, Codex, GitHub Copilot, TabNine, Claude 3 Opus, and Code Llama, with a focus on their impact on productivity, debugging correctness, and workflow efficiency. We offer concrete recommendations for developers to best leverage these tools. And even though the paper includes all kind of LLMs, I would say that the practical experimentation is target on IDE integrations (having in mind provisioning) and with the focus (actually based on another two LLMs for real-time completion) GitHub Copilot and TabNine, both extensions for Visual Studio Code. This benchmarking of their performance and usability in live coding environments provides critical insights into the capabilities and limitations of LLMs, enabling developers to strategically optimize their workflows by selecting and integrating the most intelligent tools available.