# ProCQA: A Large-scale Community-based Programming Question Answering Dataset for Code Search

Source: [ProCQA: A Large-scale Community-based Programming Question Answering Dataset for Code Search](<https://www.sota2.com/research/paper/68fa729d1e209131cea57885>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T02:30:51.475Z

Authors: Zehan Li, Jianfei Zhang, Chuantao Yin, Yuanxin Ouyang, Wenge Rong

Published: 2024-03-25T12:34:33.000Z

## Abstract

Retrieval-based code question answering seeks to match user queries in natural language to relevant code snippets. Previous approaches typically rely on pretraining models using crafted bi-modal and uni-modal datasets to align text and code representations. In this paper, we introduce ProCQA, a large-scale programming question answering dataset extracted from the StackOverflow community, offering naturally structured mixed-modal QA pairs. To validate its effectiveness, we propose a modality-agnostic contrastive pre-training approach to improve the alignment of text and code representations of current code language models. Compared to previous models that primarily employ bimodal and unimodal pairs extracted from CodeSearchNet for pre-training, our model exhibits significant performance improvements across a wide range of code retrieval benchmarks.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2403.16702>)

[PDF](<https://arxiv.org/pdf/2403.16702.pdf>)