一句话：通过分析 2829 条量子开发者论坛提问，归纳六类 QSE 挑战并构建数据集，用 ChatGPT 校验标注及 D&ML 分类器自动分类。
量子软件工程（QSE）中，开发者常在 Stack Overflow 等论坛讨论挑战，但现有量子标签偏技术实现，难以按量子概念组织问题。本文提取 2829 条量子相关开发者提问，结合定性与定量分析、内容分析和扎根理论，归纳出 Tooling、Theoretical、Learning、Conceptual、Errors 与 API Usage 等六类常见挑战，并构建人工标注数据集；同时用 ChatGPT 校验人工标注、处理分歧。随后微调 FNN、CNN、LSTM、GRU、RNN 等深度与机器学习分类器，并采用随机删除、交换和插入等文本增强提升分类效果。实验平均准确率分别为 89%、86%、84%、84%、80%。
Quantum Software Engineering (QSE) has recently emerged as a potential research and development direction frequently practiced by many tech joints. However, quantum developers face challenges in optimizing quantum computing and QSE concepts. Quantum developers use the Stack Overflow (SO) platform to report and discuss quantum-related challenges. Also, quantum practitioners use specialized quantum tags to label quantum-related posts in developers' forums. However, these quantum tags referred to more technical quantum aspects than the developer posts. Therefore, categorizing quantum practitioners' questions based on quantum concepts can help quantum developers better identify frequently occurring challenges to QSE. For this purpose, we conducted qualitative and quantitative studies to classify quantum developers' questions into various frequently occurring quantum-related challenges. We extracted 2829 developers' questions from various Q&A platforms using queries and filters based on quantum-related tags. Next, the developers' posts on the Q&A forums were critically analyzed to identify frequently discussed quantum-related challenges and develop a novel grounded theory. The frequent quantum developer challenges identified by analyzing practitioners' discussions in the Q&A forums include Tooling, Theoretical, Learning, Conceptual, Errors, and API Usage. Moreover, using content analysis and grounded theory, the developers' discussions were annotated with commonly reported quantum challenges to develop a ground truth and a novel dataset. A Large Language model (ChatGPT) was used to validate the human annotation and overcome disagreements. Finally, various fine-tuned Deep and Machine learning (D&ML) classifiers automatically classify developer discussions into commonly reported quantum challenges. Additionally, to improve the classification results, we utilized textual data augmentation approaches, such as random deletion, swapping, and insertion with the D&ML classifiers. We obtained average accuracies of 89%, 86%, 84%, 84%, and 80% with FNN, CNN, LSTM, GRU, and RNN classifiers, respectively. This helps quantum researchers and vendors propose solutions and tools to frequently occurring issues for quantum developers.
DOI 原文 ·
@inproceedings{paperbot8962,
title = {Exploring Developers Discussion Forums for Quantum Software Engineering: A Fine-Grained Classification Approach Using Large Language Model (ChatGPT)},
author = {Mobashir Husain and Muhammad Sohail Khan and Javed Ali Khan and Nek Dil Khan and Arif Khan and Muhammad Azeem Akbar},
booktitle = {FSE},
year = {2025},
doi = {10.1145/3696630.3731625}
}