{"id":"W2009151039","doi":"10.1016/j.jss.2012.07.050","title":"Towards an early software estimation using log-linear regression and a multilayer perceptron model","year":2012,"lang":"en","type":"article","venue":"Journal of Systems and Software","topic":"Software Engineering Research","field":"Computer Science","cited_by":214,"is_retracted":false,"has_abstract":false,"ca_institutions":"Western University","funders":"","keywords":"Use Case Points; Software; Computer science; Linear regression; Machine learning; Artificial neural network; Software sizing; Software development; Perceptron; Multilayer perceptron; Data mining; Software metric; Estimator; Artificial intelligence; Software development process; Software construction; Statistics; Mathematics","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":[0.000933548,0.0001842008,0.0003261705,0.0002447872,0.0001642899,0.0002315003,0.0002985828,0.000139912,0.000001358087],"category_scores_gemma":[0.0005566873,0.0001380343,0.00005514229,0.0001553539,0.00003954546,0.002122139,0.0001588589,0.0003090899,0.000001511236],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009701002,"about_ca_system_score_gemma":0.00009795061,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008093122,"about_ca_topic_score_gemma":4.852496e-7,"domain_scores_codex":[0.9983808,0.0000896636,0.0004112038,0.0001975772,0.0005699864,0.0003507516],"domain_scores_gemma":[0.9986101,0.0001745975,0.0002359162,0.0002836931,0.0003028554,0.0003928077],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000118739,0.0003365957,0.5560213,0.001162659,0.0001452164,0.0001089222,0.02116498,0.2576251,0.004595068,0.0004593178,0.0003906704,0.1578714],"study_design_scores_gemma":[0.0005487957,0.0002263948,0.06973398,0.0005437653,0.00002093556,0.0006793934,0.0001297814,0.9276459,0.00009862037,0.00008739017,0.00004384948,0.0002412119],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4966639,0.001269517,0.501701,0.00001327546,0.0002312516,0.00007087438,0.000001984687,0.00004766261,5.589578e-7],"genre_scores_gemma":[0.6897542,0.00002447083,0.3099938,0.000009073954,0.000171069,0.000001964415,5.380091e-7,0.00001688215,0.00002792782],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6700208,"threshold_uncertainty_score":0.5628875,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05075847411863618,"score_gpt":0.3211822357308679,"score_spread":0.2704237616122318,"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."}}