{"id":"W2070016297","doi":"10.4018/ijssci.2011070103","title":"Empirical Studies on the Functional Complexity of Software in Large-Scale Software Systems","year":2011,"lang":"en","type":"article","venue":"International Journal of Software Science and Computational Intelligence","topic":"Cognitive Computing and Networks","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science; Software construction; Software metric; Software system; Software sizing; Programming complexity; Software development; Software; Software analytics; Software engineering; Theoretical computer science; 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":[0.004966176,0.0003311299,0.0002898244,0.002729938,0.0007818952,0.001046555,0.0007278915,0.0004646212,0.001410964],"category_scores_gemma":[0.1067782,0.0002114722,0.0003153239,0.003498761,0.002865451,0.00346113,0.001161854,0.001054851,0.0001185982],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008995986,"about_ca_system_score_gemma":0.0005417451,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001889522,"about_ca_topic_score_gemma":0.002497767,"domain_scores_codex":[0.9950234,0.001874737,0.0003580511,0.0005137387,0.00202891,0.0002012037],"domain_scores_gemma":[0.724534,0.2306211,0.02535473,0.007823902,0.01000107,0.001665335],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001841265,0.0007518333,0.8735676,0.0004128464,0.0002821788,0.0003349566,0.008102578,0.02459445,0.00266617,0.02622933,0.0009212182,0.06195269],"study_design_scores_gemma":[0.00002333419,0.0004521364,0.9320896,0.0001168411,0.00006382186,0.0005092787,0.005525448,0.03654162,0.002054156,0.01974597,0.002827563,0.00005027267],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9900178,0.000210999,0.007673748,0.0001432364,0.00000296681,0.00002815392,0.00007793001,0.000009098876,0.00183609],"genre_scores_gemma":[0.9980224,0.000110211,0.001606713,0.00001415015,0.000007246132,0.00002632571,0.0001201547,0.000004911271,0.00008777395],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004966176,"threshold_uncertainty_score":0.02626395,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1654289918631549,"score_gpt":0.3517204265598767,"score_spread":0.1862914346967218,"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."}}