# Uncovering Intention through LLM-Driven Code Snippet Description Generation

arXiv: https://arxiv.org/abs/2506.15453 · HTML: https://mlsanity.com/paper/2506.15453/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 29 references that are in the map.
- First posted on arXiv 18 Jun 2025.

## Builds on (most cited first, 15 of 15)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [A Survey on Large Language Models for Code Generation](https://mlsanity.com/paper/2406.00515/) (2024)
- [Prompt Programming for Large Language Models: Beyond the Few-Shot Paradigm](https://mlsanity.com/paper/2102.07350/) (2021)
- [Software Testing with Large Language Models: Survey, Landscape, and Vision](https://mlsanity.com/paper/2307.07221/) (2023)
- [Bias Testing and Mitigation in LLM-based Code Generation](https://mlsanity.com/paper/2309.14345/) (2023)
- [Large Language Models for Code Analysis: Do LLMs Really Do Their Job?](https://mlsanity.com/paper/2310.12357/) (2023)
- [Categorizing the Content of GitHub README Files](https://mlsanity.com/paper/1802.06997/) (2018)
- [From Tarzan to Tolkien: Controlling the Language Proficiency Level of LLMs for Content Generation](https://mlsanity.com/paper/2406.03030/) (2024)
- [Evaluating Diverse Large Language Models for Automatic and General Bug Reproduction](https://mlsanity.com/paper/2311.04532/) (2023)
- [AUTOGENICS: Automated Generation of Context-Aware Inline Comments for Code Snippets on Programming Q&A Sites Using LLM](https://mlsanity.com/paper/2408.15411/) (2024)
- [Let's Discover More API Relations: A Large Language Model-based AI Chain for Unsupervised API Relation Inference](https://mlsanity.com/paper/2311.01266/) (2023)
- [NCQ: Code reuse support for node.js developers](https://mlsanity.com/paper/2101.00756/) (2021)
- [LARCH: Large Language Model-based Automatic Readme Creation with Heuristics](https://mlsanity.com/paper/2308.03099/) (2023)
- [An Empirical Study on README contents for JavaScript Packages](https://mlsanity.com/paper/1802.08391/) (2018)
- [How are Project-Specific Forums Utilized? A Study of Participation, Content, and Sentiment in the Eclipse Ecosystem](https://mlsanity.com/paper/2009.09130/) (2020)

## Built on by (most cited first, 1 of 1)

- [CIAO - Code In Architecture Out - Automated Software Architecture Documentation with Large Language Models](https://mlsanity.com/paper/2604.08293/) (2026)

## Abstract (arXiv)

Documenting code snippets is essential to pinpoint key areas where both developers and users should pay attention. Examples include usage examples and other Application Programming Interfaces (APIs), which are especially important for third-party libraries. With the rise of Large Language Models (LLMs), the key goal is to investigate the kinds of description developers commonly use and evaluate how well an LLM, in this case Llama, can support description generation. We use NPM Code Snippets, consisting of 185,412 packages with 1,024,579 code snippets. From there, we use 400 code snippets (and their descriptions) as samples. First, our manual classification found that the majority of original descriptions (55.5%) highlight example-based usage. This finding emphasizes the importance of clear documentation, as some descriptions lacked sufficient detail to convey intent. Second, the LLM correctly identified the majority of original descriptions as "Example" (79.75%), which is identical to our manual finding, showing a propensity for generalization. Third, compared to the originals, the produced description had an average similarity score of 0.7173, suggesting relevance but room for improvement. Scores below 0.9 indicate some irrelevance. Our results show that depending on the task of the code snippet, the intention of the document may differ from being instructions for usage, installations, or descriptive learning examples for any user of a library.