Data and results for the paper: "From Bugs to Benefits: Leveraging Crowd-Sourced Issue Data to Improve User Stories with CrUISE-AC"

This page provided the following files used in the study "From Bugs to Benefits: Leveraging Crowd-Sourced Issue Data to Improve User Stories with CrUISE-AC". domain_thesaurus.json: basic manually created thesaurus for terms that are used in the eCommerce domain. Words are POS tagged ($n for nouns, $v for verbs and $a for adjectives). Composites are combined by underscore. generated_acceptance_criteria.xlsx: automatically generated and assessed acceptance criteria along with a manual approval. The columns are ID: unique ID of the user story Connextra: user story written in connextra pattern "As a [role], I [what], {so I [benefit]}" Acceptance Criteria: Acceptance criteria that supported the user story originally Metric class: class of user story determined by CrUISE-score metric IssueID: unique ID of an issue, the acceptance criteria was generated from Issue Text Preprocessed: the issue text after beeing preprocessed is supposed to contain requirement-relevant information only Generated Acceptance Criteria: Gherkin-style acceptance criteria generated from [Issue Text Preprocessed] Explanation: Explanation generated by GPT-4 Turbo why this acceptance criteria is a valuable and non-trivial addition to the user story and its original AC Relevant: 1 if manual inspection approved this acceptance criteria as valuable and non-trivial, otherwise 0 userstories_metric_classes.xlsx: metric class, explanations and suggestions generated by CrUISE-score