{"id":"W3194111178","doi":"10.1007/s00766-021-00360-6","title":"A validation of QDAcity-RE for domain modeling using qualitative data analysis","year":2021,"lang":"en","type":"article","venue":"Requirements Engineering","topic":"Software Engineering Research","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Deutsche Forschungsgemeinschaft; Friedrich-Alexander-Universität Erlangen-Nürnberg","keywords":"Traceability; Domain (mathematical analysis); Computer science; Set (abstract data type); Data mining; Artificial intelligence; Machine learning; Mathematics; Software engineering; Programming language","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.1441586,0.001312354,0.0009449935,0.004229848,0.00181275,0.004355114,0.00328896,0.001675541,0.0041559],"category_scores_gemma":[0.2854567,0.0008385969,0.001206463,0.002425878,0.003663118,0.004215056,0.005491535,0.002224287,0.0009850388],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003708157,"about_ca_system_score_gemma":0.0053882,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003673242,"about_ca_topic_score_gemma":0.002875536,"domain_scores_codex":[0.8712289,0.0933551,0.007075565,0.00786217,0.01941318,0.001065155],"domain_scores_gemma":[0.4808267,0.3496211,0.01111214,0.09194198,0.06476314,0.001734967],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.002499286,0.005192743,0.04972337,0.005778991,0.0006979228,0.0008527463,0.04729341,0.06372481,0.07742477,0.09738431,0.006996498,0.6424311],"study_design_scores_gemma":[0.001484536,0.004003456,0.04270984,0.003497432,0.0003088253,0.001156604,0.02491482,0.6619432,0.140765,0.05634819,0.06214371,0.0007244384],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1287965,0.0001196083,0.8509747,0.0007912534,0.0001468792,0.006021189,0.001194949,0.002514414,0.009440564],"genre_scores_gemma":[0.304248,0.0000481163,0.6883318,0.0001873084,0.00001028744,0.005253628,0.0008025351,0.0002690528,0.0008493368],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.1441586,"threshold_uncertainty_score":0.7623928,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2265846632577237,"score_gpt":0.4207023133501867,"score_spread":0.1941176500924629,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}