A Review of Repository Level Prompting for LLMs

• URL: http://arxiv.org/abs/2312.10101v1
• Date: Fri, 15 Dec 2023 00:34:52 GMT
• Title: A Review of Repository Level Prompting for LLMs
• Authors: Douglas Schonholtz
• Abstract summary: Large Language Models (LLMs) have led to notable successes, such as achieving a 94.6% solve rate on the HumanEval benchmark. There is an increasing commercial push for repository-level inline code completion tools, such as GitHub Copilot and Tab Nine. This paper delves into the transition from individual coding problems to repository-scale solutions.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: As coding challenges become more complex, recent advancements in Large Language Models (LLMs) have led to notable successes, such as achieving a 94.6\% solve rate on the HumanEval benchmark. Concurrently, there is an increasing commercial push for repository-level inline code completion tools, such as GitHub Copilot and Tab Nine, aimed at enhancing developer productivity. This paper delves into the transition from individual coding problems to repository-scale solutions, presenting a thorough review of the current literature on effective LLM prompting for code generation at the repository level. We examine approaches that will work with black-box LLMs such that they will be useful and applicable to commercial use cases, and their applicability in interpreting code at a repository scale. We juxtapose the Repository-Level Prompt Generation technique with RepoCoder, an iterative retrieval and generation method, to highlight the trade-offs inherent in each approach and to establish best practices for their application in cutting-edge coding benchmarks. The interplay between iterative refinement of prompts and the development of advanced retrieval systems forms the core of our discussion, offering a pathway to significantly improve LLM performance in code generation tasks. Insights from this study not only guide the application of these methods but also chart a course for future research to integrate such techniques into broader software engineering contexts.

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