{"id":"W2364021410","doi":"10.5220/0005857601720180","title":"Source and Test Code Size Prediction - A Comparison between Use Case Metrics and Objective Class Points","year":2016,"lang":"en","type":"article","venue":"","topic":"Software Engineering Research","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec","funders":"","keywords":"Computer science; Metric (unit); Regression testing; Source lines of code; Source code; Java; Software metric; Data mining; Linear regression; Test case; Parametric statistics; Regression analysis; Code (set theory); Software; Software quality; Machine learning; Software development; Statistics; Set (abstract data type); Programming language; Mathematics; Software construction","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":[0.00834127,0.0008260486,0.0005122235,0.005234775,0.0002025156,0.0009951688,0.0009690749,0.000812367,0.001108862],"category_scores_gemma":[0.07947439,0.0003270498,0.000656736,0.003605703,0.0003882344,0.002486317,0.0008521496,0.0007951887,0.0006670232],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006363222,"about_ca_system_score_gemma":0.0004222727,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002453292,"about_ca_topic_score_gemma":0.003424629,"domain_scores_codex":[0.9909229,0.003159961,0.0006199827,0.001332399,0.003717607,0.0002472755],"domain_scores_gemma":[0.8472067,0.1139326,0.01863262,0.007551066,0.01150247,0.001174511],"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":[0.0003715442,0.0004882306,0.8322721,0.0001910446,0.0002346605,0.00009304856,0.0006204673,0.02160944,0.002293743,0.0005962322,0.00103743,0.1401922],"study_design_scores_gemma":[0.00002687209,0.0007023045,0.7598414,0.00006750812,0.00006865575,0.0002159209,0.0003290933,0.23182,0.004470652,0.001014624,0.001390047,0.00005298823],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.963897,0.0003348911,0.03111898,0.0001184716,0.00001759862,0.0001032445,0.001265051,0.0006735412,0.002471119],"genre_scores_gemma":[0.9875246,0.00007607513,0.01010476,0.00001660543,0.00001260759,0.0001104063,0.001522773,0.0000697334,0.0005624389],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00834127,"threshold_uncertainty_score":0.04411334,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0340022424404547,"score_gpt":0.2800428718051099,"score_spread":0.2460406293646553,"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."}}