{"id":"W3091435513","doi":"10.1145/3365438.3410953","title":"Leveraging natural-language requirements for deriving better acceptance criteria from models","year":2020,"lang":"en","type":"article","venue":"","topic":"Software Engineering Research","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada; Fonds National de la Recherche Luxembourg","keywords":"Computer science; Requirements analysis; Natural language; Software requirements specification; Non-functional testing; Requirements elicitation; Acceptance testing; Software engineering; Information system; System requirements specification; Software requirements; System requirements; Software; Non-functional requirement; Information retrieval; Software system; Software development; Artificial intelligence; Programming language; Software design; Engineering; Software construction","routes":{"ca_aff":true,"ca_fund":true,"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":[0.02253885,0.002579668,0.001575346,0.008138712,0.001100564,0.004648197,0.003322863,0.00339638,0.002265568],"category_scores_gemma":[0.1282651,0.002333662,0.005061804,0.003650297,0.002923942,0.009957024,0.005161854,0.005157685,0.001039414],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002440506,"about_ca_system_score_gemma":0.004440899,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003063947,"about_ca_topic_score_gemma":0.005977674,"domain_scores_codex":[0.9529718,0.02352188,0.00364991,0.002614122,0.01635006,0.0008922061],"domain_scores_gemma":[0.8901216,0.07612843,0.007283701,0.01455077,0.01139816,0.0005172873],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002573369,0.0009053687,0.006151629,0.001952851,0.0004545963,0.003139614,0.008262073,0.3002441,0.03439669,0.4293639,0.003887389,0.2109846],"study_design_scores_gemma":[0.00007291036,0.0002063869,0.0008422706,0.0007250064,0.0001819443,0.0006612074,0.0006408861,0.7223336,0.01981065,0.2342194,0.02012258,0.0001831287],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.005601614,0.00006376614,0.9919063,0.0003349691,0.00001336158,0.0002461658,0.0001317022,0.0005238188,0.001178266],"genre_scores_gemma":[0.08987807,0.0001657061,0.9071845,0.0002482524,0.00003388278,0.0007351119,0.0008217026,0.000460262,0.0004726014],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02253885,"threshold_uncertainty_score":0.1191983,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06341128614539714,"score_gpt":0.316385960359451,"score_spread":0.2529746742140538,"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."}}