STORY2DIFF: A Dataset Linking Real-World User Stories to Code Changes

Benedetta Donato, Daniele Franchini, Alessandro Greco, Oliviero Riganelli, Daniela Micucci +1

Research on requirements-to-code tasks needs examples in which a genuine requirement is paired with the change that implemented it. Existing user-story collections, however, largely contain isolated or synthetic text. To address this limitation, we present STORY2DIFF, a dataset of 7,785 English user stories linked to merged pull requests from January 2017 to July 2026. The dataset is mined from a set of 982 public Java and Python repositories. Each record in the dataset combines the original issue, extracted Connextra elements, the associated pull request and commit metadata, a file-level diff capturing the code changes introduced by the associated pull request, and the affected files before and after the change. The tool we built to mine STORY2DIFF can be used to repeat the automatic extraction of the records, using different parameters (e.g., a different timeframe). The dataset can support a variety of tasks, including requirement discovery, traceability, change- impact estimation, and requirements-aware code generation. Dataset: https://github.com/riganelli/user-story-mining-pipeline/ releases/tag/V4.0.1. Video: https://youtu.be/qOhXUeyeiTQ.