Supporting Material for Paper "Revised CrUISE-AC for User-Story Enrichment: Learning from Crowd Knowledge in Issue Trackers—Laboratory and Field Evaluation"

The following files are provided as supplementary material for our paper "Revised CrUISE-AC for User-Story Enrichment: Learning from Crowd Knowledge in Issue Trackers—Laboratory and Field Evaluation", submitted to the Empirical Software Engineering journal. Closing Interviews General interview guideline in German and an English version (automatic translation, manually reviewed) Four closing interviews with product owners (POs) in German, including English translations and annotated versions created during qualitative analysis Coding schema (hierarchical), created using MAXQDA 24 Performance Analysis Performance results for the baseline and revised approaches for e-commerce and CMS user stories Includes step-level timings per user story Expert Assessments Assessments by experts E1 and E2 of generated acceptance criteria for sampled e-commerce and CMS user stories Prompts prompt_extract_userstory: extract or generate a user story from a requirements document (field experiment) prompt_match_layer_1_and_2: matching prompt used in layers 1 and 2 prompt_match_layer_3: matching prompt used in layer 3 prompt_generate: generate an acceptance criterion from an issue prompt_deduplication: detect duplicates in generated acceptance criteria prompt_evaluation: detect irrelevant acceptance criteria prompt_novelty_classification: classify acceptance criteria by degree of novelty app domains: short description of application domains as inserted into the prompts Questionnaire Questionnaire as presented to the POs in German and English (automatic translation, manually reviewed)