# StaQC: A Systematically Mined Question-Code Dataset from Stack Overflow

arXiv: https://arxiv.org/abs/1803.09371 · HTML: https://mlsanity.com/paper/1803.09371/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Mar 2018.

## Built on by (most cited first, 30 of 30)

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- [ProCQA: A Large-scale Community-based Programming Question Answering Dataset for Code Search](https://mlsanity.com/paper/2403.16702/) (2024)
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- [CoSQA+: Pioneering the Multi-Choice Code Search Benchmark with Test-Driven Agents](https://mlsanity.com/paper/2406.11589/) (2024)
- [Reading StackOverflow Encourages Cheating: Adding Question Text Improves Extractive Code Generation](https://mlsanity.com/paper/2106.04447/) (2021)
- [Code Generation from Natural Language with Less Prior and More Monolingual Data](https://mlsanity.com/paper/2101.00259/) (2021)
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- [A Survey on Artificial Intelligence for Source Code: A Dialogue Systems Perspective](https://mlsanity.com/paper/2202.04847/) (2022)
- [Neural Machine Translation for Code Generation](https://mlsanity.com/paper/2305.13504/) (2023)
- [JEMMA: An Extensible Java Dataset for ML4Code Applications](https://mlsanity.com/paper/2212.09132/) (2022)
- [An Exploratory Literature Study on Sharing and Energy Use of Language Models for Source Code](https://mlsanity.com/paper/2307.02443/) (2023)
- [Refining Joint Text and Source Code Embeddings for Retrieval Task with Parameter-Efficient Fine-Tuning](https://mlsanity.com/paper/2405.04126/) (2024)
- [Teach me how to Label: Labeling Functions from Natural Language with Text-to-text Transformers](https://mlsanity.com/paper/2101.07138/) (2021)
- [Text Classification for Task-based Source Code Related Questions](https://mlsanity.com/paper/2111.00580/) (2021)
- [A Machine Learning Approach Towards SKILL Code Autocompletion](https://mlsanity.com/paper/2312.01921/) (2023)
- [A Hybrid LTR-based System via Social Context Embedding for Recommending Solutions of Software Bugs in Developer Communities](https://mlsanity.com/paper/2603.07229/) (2026)

## Abstract (arXiv)

Stack Overflow (SO) has been a great source of natural language questions and their code solutions (i.e., question-code pairs), which are critical for many tasks including code retrieval and annotation. In most existing research, question-code pairs were collected heuristically and tend to have low quality. In this paper, we investigate a new problem of systematically mining question-code pairs from Stack Overflow (in contrast to heuristically collecting them). It is formulated as predicting whether or not a code snippet is a standalone solution to a question. We propose a novel Bi-View Hierarchical Neural Network which can capture both the programming content and the textual context of a code snippet (i.e., two views) to make a prediction. On two manually annotated datasets in Python and SQL domain, our framework substantially outperforms heuristic methods with at least 15% higher F1 and accuracy. Furthermore, we present StaQC (Stack Overflow Question-Code pairs), the largest dataset to date of ~148K Python and ~120K SQL question-code pairs, automatically mined from SO using our framework. Under various case studies, we demonstrate that StaQC can greatly help develop data-hungry models for associating natural language with programming language.