# Analyzing and Supporting Adaptation of Online Code Examples

arXiv: https://arxiv.org/abs/1905.12111 · HTML: https://mlsanity.com/paper/1905.12111/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 May 2019.

## Built on by (most cited first, 8 of 8)

- [Instruct or Interact? Exploring and Eliciting LLMs' Capability in Code Snippet Adaptation Through Prompt Engineering](https://mlsanity.com/paper/2411.15501/) (2024)
- [ZS4C: Zero-Shot Synthesis of Compilable Code for Incomplete Code Snippets using LLMs](https://mlsanity.com/paper/2401.14279/) (2024)
- [AdaptEval: A Benchmark for Evaluating Large Language Models on Code Snippet Adaptation](https://mlsanity.com/paper/2601.04540/) (2026)
- [RAILS: Retrieval-Augmented Intelligence for Learning Software Development](https://mlsanity.com/paper/2506.22742/) (2025)
- [Wired for Reuse: Automating Context-Aware Code Adaptation in IDEs via LLM-Based Agent](https://mlsanity.com/paper/2507.01315/) (2025)
- [Cerberus: Multi-Agent Reasoning and Coverage-Guided Exploration for Static Detection of Runtime Errors](https://mlsanity.com/paper/2512.21431/) (2025)
- [Coding in a Bubble? Evaluating LLMs in Resolving Context Adaptation Bugs During Code Adaptation](https://mlsanity.com/paper/2601.06497/) (2026)
- [AdaptAgent: A Multi-agent, Domain-Guided Reasoning Framework for Code Adaptation](https://mlsanity.com/paper/2608.04459/) (2026)

## Abstract (arXiv)

Developers often resort to online Q&A forums such as Stack Overflow (SO) for filling their programming needs. Although code examples on those forums are good starting points, they are often incomplete and inadequate for developers' local program contexts; adaptation of those examples is necessary to integrate them to production code. As a consequence, the process of adapting online code examples is done over and over again, by multiple developers independently. Our work extensively studies these adaptations and variations, serving as the basis for a tool that helps integrate these online code examples in a target context in an interactive manner. We perform a large-scale empirical study about the nature and extent of adaptations and variations of SO snippets. We construct a comprehensive dataset linking SO posts to GitHub counterparts based on clone detection, time stamp analysis, and explicit URL references. We then qualitatively inspect 400 SO examples and their GitHub counterparts and develop a taxonomy of 24 adaptation types. Using this taxonomy, we build an automated adaptation analysis technique on top of GumTree to classify the entire dataset into these types. We build a Chrome extension called ExampleStack that automatically lifts an adaptation-aware template from each SO example and its GitHub counterparts to identify hot spots where most changes happen. A user study with sixteen programmers shows that seeing the commonalities and variations in similar GitHub counterparts increases their confidence about the given SO example, and helps them grasp a more comprehensive view about how to reuse the example differently and avoid common pitfalls.