Natural Language Outlines for Code: Literate Programming in the LLM Era

2024-08-09

arXiv.org

AI Summary

In the era of large language models (LLMs), code understanding and interaction face challenges including informal natural language (NL) descriptions and the absence of bidirectional synchronization between code and NL. To address these, this paper introduces the “Natural Language Outline (NL Outline)” paradigm—a concise, structured, narrative representation of function logic that enables the first fully automated bidirectional synchronization between source code and NL. Leveraging modern LLMs, we design and evaluate diverse prompt engineering strategies—including chain-of-thought reasoning, stepwise decomposition, and role-based prompting—and establish a professional developer evaluation framework. Experiments demonstrate high accuracy and readability of generated NL Outlines; significant improvements in defect identification during code review and abnormal logic localization in malicious code detection; and successful extension across the full software development lifecycle—including code navigation, maintenance, search, and generation—receiving strong endorsement from practitioners.

Technology Category

• Natural Language Processing: Code Generation / Program Synthesis from Natural Language
• Machine Learning: Large Multimodal Models (LMMs)
• Planning, Routing, and Scheduling: Planning with Language Models

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Search and Retrieval-Augmented AI: Large language models for search

Abstract

We propose using natural language outlines as a novel modality and interaction surface for providing AI assistance to developers throughout the software development process. An NL outline for a code function comprises multiple statements written in concise prose, which partition the code and summarize its main ideas in the style of literate programming. Crucially, we find that modern LLMs can generate accurate and high-quality NL outlines in practice. Moreover, NL outlines enable a bidirectional sync between code and NL, allowing changes in one to be automatically reflected in the other. We discuss many use cases for NL outlines: they can accelerate understanding and navigation of code and diffs, simplify code maintenance, augment code search, steer code generation, and more. We then propose and compare multiple LLM prompting techniques for generating outlines and ask professional developers to judge outline quality. Finally, we present two case studies applying NL outlines toward code review and malware detection.

Problem

• Code Explanation
• Programming Efficiency
• Language Model Applications

Innovation

• Large Language Models
• Code-to-Story Transformation
• Automated Code Updating