LLM-Generated Software Requirements from GitHub Issues

This dataset contains software requirements automatically generated from bug reports and feature requests extracted from the three most popular machine learning repositories on GitHub: Scikit-learn, TensorFlow, and Transformers. The dataset is structured into issue data, generated requirements, and evaluations based on three well-defined criteria. Dataset Structure issues.csv: Contains issue titles along with their corresponding repository names and unique identifiers. Requirements Files: These files store the requirements generated by LLMs for each issue, categorized by different prompting methods: few_shot_requirements.csv zero_shot_requirements.csv expert_requirements.csv expert_few_shot_requirements.csv Evaluation Files: These files contain the assessment of the generated requirements based on three key quality criteria: Unambiguity, Understandability, and Singularity. The evaluations are also divided by prompting methods: few_shot_evaluation.csv zero_shot_evaluation.csv expert_evaluation.csv expert_few_shot_evaluation.csv本数据集包含从GitHub平台上三款最热门的机器学习开源仓库——Scikit-learn、TensorFlow与Transformers——中提取的缺陷报告与功能请求，并基于这些内容自动生成软件需求。本数据集分为议题数据、生成的需求以及基于三项明确定义的质量标准的评估三个部分。 数据集结构 issues.csv：存储议题标题及其所属仓库名称与唯一标识符。 需求文件：此类文件存储大语言模型（Large Language Model）为各议题生成的软件需求，并按照不同提示方法进行分类： few_shot_requirements.csv（少样本提示需求文件） zero_shot_requirements.csv（零样本提示需求文件） expert_requirements.csv（专家提示需求文件） expert_few_shot_requirements.csv（专家少样本提示需求文件） 评估文件：此类文件包含基于三项核心质量标准——明确性（Unambiguity）、可理解性（Understandability）与单一性（Singularity）——对生成的软件需求开展的评估结果，且同样按照提示方法进行分类： few_shot_evaluation.csv（少样本提示评估文件） zero_shot_evaluation.csv（零样本提示评估文件） expert_evaluation.csv（专家提示评估文件） expert_few_shot_evaluation.csv（专家少样本提示评估文件）