Optimising the Fit of Stack Overflow Code Snippets into Existing Code
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Abstract (arXiv)
Software developers often reuse code from online sources such as Stack Overflow within their projects. However, the process of searching for code snippets and integrating them within existing source code can be tedious. In order to improve efficiency and reduce time spent on code reuse, we present an automated code reuse tool for the Eclipse IDE (Integrated Developer Environment), NLP2TestableCode. NLP2TestableCode can not only search for Java code snippets using natural language tasks, but also evaluate code snippets based on a user's existing code, modify snippets to improve fit and correct errors, before presenting the user with the best snippet, all without leaving the editor. NLP2TestableCode also includes functionality to automatically generate customisable test cases and suggest argument and return types, in order to further evaluate code snippets. In evaluation, NLP2TestableCode was capable of finding compilable code snippets for 82.9% of tasks, and testable code snippets for 42.9%.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). First posted on arXiv 16 Apr 2020.
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Citing papers per year: 2022: 1; 2023: 0; 2024: 1; 2025: 1; 2026: 2.
5 papers.
- 2024Instruct or Interact? Exploring and Eliciting LLMs' Capability in Code Snippet Adaptation Through Prompt Engineeringcited by 6
- 2022A Survey on Artificial Intelligence for Source Code: A Dialogue Systems Perspectivecited by 3
- 2025Wired for Reuse: Automating Context-Aware Code Adaptation in IDEs via LLM-Based Agentcited by 0
- 2026Coding in a Bubble? Evaluating LLMs in Resolving Context Adaptation Bugs During Code Adaptationcited by 0
- 2026AdaptAgent: A Multi-agent, Domain-Guided Reasoning Framework for Code Adaptationcited by 0
- 2026AdaptAgent: A Multi-agent, Domain-Guided Reasoning Framework for Code Adaptation5 Aug 2026
- 2026Coding in a Bubble? Evaluating LLMs in Resolving Context Adaptation Bugs During Code Adaptation10 Jan 2026
- 2025Wired for Reuse: Automating Context-Aware Code Adaptation in IDEs via LLM-Based Agent2 Jul 2025
- 2024Instruct or Interact? Exploring and Eliciting LLMs' Capability in Code Snippet Adaptation Through Prompt Engineering23 Nov 2024
- 2022A Survey on Artificial Intelligence for Source Code: A Dialogue Systems Perspective10 Feb 2022