CodeInsight: A Curated Dataset of Practical Coding Solutions from Stack Overflow
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Abstract (arXiv)
We introduce a novel dataset tailored for code generation, aimed at aiding developers in common tasks. Our dataset provides examples that include a clarified intent, code snippets associated, and an average of three related unit tests. It encompasses a range of libraries such as textttPandas, textttNumpy, and textttRegex, along with more than 70 standard libraries in Python code derived from Stack Overflow. Comprising 3,409 crafted examples by Python experts, our dataset is designed for both model finetuning and standalone evaluation. To complete unit tests evaluation, we categorize examples in order to get more fine grained analysis, enhancing the understanding of models' strengths and weaknesses in specific coding tasks. The examples have been refined to reduce data contamination, a process confirmed by the performance of three leading models: Mistral 7B, CodeLLaMa 13B, and Starcoder 15B. We further investigate data-contamination testing GPT-4 performance on a part of our dataset. The benchmark can be accessed at urlhttps://github.com/NathanaelBeau/CodeInsight.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 11 of its 15 references that are in the map. First posted on arXiv 25 Sep 2024.
Builds on
11 of 15 references.
References · 11
- 2021Evaluating Large Language Models Trained on Codecited by 8,320
- 2021Program Synthesis with Large Language Modelscited by 3,637
- 2023Code Llama: Open Foundation Models for Codecited by 2,139
- 2021Measuring Coding Challenge Competence With APPScited by 902
- 2023StarCoder: may the source be with you!cited by 799
- 2020CodeBLEU: a Method for Automatic Evaluation of Code Synthesiscited by 479
- 2022DS-1000: A Natural and Reliable Benchmark for Data Science Code Generationcited by 452
- 2018Learning to Mine Aligned Code and Natural Language Pairs from Stack Overflowcited by 165
- 2022Execution-Based Evaluation for Open-Domain Code Generationcited by 91
- 2022Training and Evaluating a Jupyter Notebook Data Science Assistantcited by 31
- 2022The impact of lexical and grammatical processing on generating code from natural languagecited by 6
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Citing papers per year: 2025: 3; 2026: 1.
4 papers.
- 2025From Code Foundation Models to Agents and Applications: A Comprehensive Survey and Practical Guide to Code Intelligencecited by 15
- 2025GiFT: Gibbs Fine-Tuning for Code Generationcited by 3
- 2025Library Hallucinations in LLM-Generated Code: A Risk Analysis Grounded in Developer Queriescited by 1
- 2026From Competition to Collaboration: Designing Sustainable Mechanisms Between LLMs and Online Forumscited by 0
- 2026From Competition to Collaboration: Designing Sustainable Mechanisms Between LLMs and Online Forums4 Feb 2026
- 2025From Code Foundation Models to Agents and Applications: A Comprehensive Survey and Practical Guide to Code Intelligence23 Nov 2025
- 2025Library Hallucinations in LLM-Generated Code: A Risk Analysis Grounded in Developer Queries26 Sep 2025
- 2025GiFT: Gibbs Fine-Tuning for Code Generation17 Feb 2025