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