Summary of Codenav: Beyond Tool-use to Using Real-world Codebases with Llm Agents, by Tanmay Gupta et al.
CodeNav: Beyond tool-use to using real-world codebases with LLM agents
by Tanmay Gupta, Luca Weihs, Aniruddha Kembhavi
First submitted to arxiv on: 18 Jun 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); Software Engineering (cs.SE)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary CodeNav is an artificial intelligence (AI) agent that utilizes previously unseen code repositories to solve user queries. Unlike traditional AI agents, CodeNav automatically indexes and searches over code blocks in the target codebase, finds relevant code snippets, imports them, and uses them to generate a solution with execution feedback. In this paper, we showcase three case studies where CodeNav solves complex user queries using diverse codebases. Additionally, we compare the effectiveness of code-use (which only has access to the target codebase) to tool-use (which has privileged access to all tool names and descriptions) on three benchmarks. Furthermore, we study the impact of varying kinds of tool and library descriptions on code-use performance, as well as investigate the advantage of the agent seeing source code as opposed to natural descriptions of code. The results demonstrate the capabilities of CodeNav in leveraging code repositories for problem-solving. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary CodeNav is a special kind of artificial intelligence that helps people find answers by looking at codes that are not familiar to it. Unlike other AI agents, CodeNav can automatically find important parts of these unfamiliar codes and use them to solve problems. In this paper, we show how CodeNav works by solving three real-life problems using different sets of code. We also compare its ability to find solutions with another way of using code that has more information about the tools used. Finally, we investigate what happens when the AI agent sees code written in a language it doesn’t know versus when it sees descriptions of these codes. |
