{"id":"W2100765983","doi":"10.1109/qsic.2007.4385497","title":"Automatic Quality Assessment of SRS Text by Means of a Decision-Tree-Based Text Classifier","year":2007,"lang":"en","type":"article","venue":"","topic":"Software Engineering Research","field":"Computer Science","cited_by":41,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Classifier (UML); Ambiguity; Decision tree; Software quality; Decision tree learning; Software; Quality (philosophy); Natural language; Artificial intelligence; Software requirements; Software requirements specification; Software engineering; Natural language processing; Information retrieval; Data mining; Software development; Software construction; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002690021,0.0001423534,0.0003073535,0.0002660037,0.00003261199,0.00003854172,0.001065711,0.00008499562,0.0002029848],"category_scores_gemma":[0.001008631,0.0001181998,0.0001088877,0.0008615651,0.00008675702,0.0001658156,0.0001942341,0.0001673717,0.00001715666],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009884873,"about_ca_system_score_gemma":0.0002634985,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001333616,"about_ca_topic_score_gemma":0.00004496378,"domain_scores_codex":[0.9973494,0.00008526761,0.0006527312,0.0003324156,0.001214933,0.0003652913],"domain_scores_gemma":[0.9930928,0.005377447,0.0001374326,0.001007057,0.0002278005,0.0001574441],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002534001,0.001227631,0.1396847,0.000432144,0.0001161598,0.00002568627,0.0004304912,0.002768827,0.02430399,0.02802623,0.01155923,0.7913996],"study_design_scores_gemma":[0.0008350512,0.000200598,0.4771578,0.0001049078,0.000005394625,0.000001973883,0.00004774141,0.4927358,0.02763185,0.0004027917,0.0006320925,0.0002440283],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2098845,0.00002611477,0.7876565,0.0001241508,0.00010103,0.0001506136,0.000003551923,0.0001570274,0.001896537],"genre_scores_gemma":[0.7149203,8.67026e-7,0.2848707,0.00004165529,0.000006086304,0.000005463137,0.000001166637,0.000008381241,0.0001454131],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7911556,"threshold_uncertainty_score":0.4820048,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03732469825907472,"score_gpt":0.3623008992789604,"score_spread":0.3249762010198857,"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."}}