Stack Overflow in Github: Any Snippets There?
Not on the map. Why?
Abstract (arXiv)
When programmers look for how to achieve certain programming tasks, Stack Overflow is a popular destination in search engine results. Over the years, Stack Overflow has accumulated an impressive knowledge base of snippets of code that are amply documented. We are interested in studying how programmers use these snippets of code in their projects. Can we find Stack Overflow snippets in real projects? When snippets are used, is this copy literal or does it suffer adaptations? And are these adaptations specializations required by the idiosyncrasies of the target artifact, or are they motivated by specific requirements of the programmer? The large-scale study presented on this paper analyzes 909k non-fork Python projects hosted on Github, which contain 290M function definitions, and 1.9M Python snippets captured in Stack Overflow. Results are presented as quantitative analysis of block-level code cloning intra and inter Stack Overflow and GitHub, and as an analysis of programming behaviors through the qualitative analysis of our findings.
Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). First posted on arXiv 2 May 2017.
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Citing papers per year: 2021: 1; 2022: 0; 2023: 0; 2024: 2; 2025: 0; 2026: 2.
5 papers.
- 2024Instruct or Interact? Exploring and Eliciting LLMs' Capability in Code Snippet Adaptation Through Prompt Engineeringcited by 6
- 2021Can Machines Read Coding Manuals Yet? -- A Benchmark for Building Better Language Models for Code Understandingcited by 5
- 2024Can LLM Prompting Serve as a Proxy for Static Analysis in Vulnerability Detectioncited by 4
- 2026AdaptEval: A Benchmark for Evaluating Large Language Models on Code Snippet Adaptationcited by 1
- 2026Coding in a Bubble? Evaluating LLMs in Resolving Context Adaptation Bugs During Code Adaptationcited by 0
- 2026Coding in a Bubble? Evaluating LLMs in Resolving Context Adaptation Bugs During Code Adaptation10 Jan 2026
- 2026AdaptEval: A Benchmark for Evaluating Large Language Models on Code Snippet Adaptation8 Jan 2026
- 2024Can LLM Prompting Serve as a Proxy for Static Analysis in Vulnerability Detection16 Dec 2024
- 2024Instruct or Interact? Exploring and Eliciting LLMs' Capability in Code Snippet Adaptation Through Prompt Engineering23 Nov 2024
- 2021Can Machines Read Coding Manuals Yet? -- A Benchmark for Building Better Language Models for Code Understanding15 Sep 2021