{"id":"W1491093693","doi":"10.1007/978-3-642-13428-9_5","title":"Empirical Evaluation of Selected Algorithms for Complexity-Based Classification of Software Modules and a New Model","year":2010,"lang":"en","type":"book-chapter","venue":"Studies in computational intelligence","topic":"Software Engineering Research","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University","funders":"","keywords":"Machine learning; Computer science; Artificial intelligence; Software quality; Data mining; Support vector machine; Software; Classifier (UML); Linear discriminant analysis; Software metric; Naive Bayes classifier; Categorization; Statistical classification; Algorithm; Software development","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":[0.02083345,0.001584138,0.001307044,0.005939205,0.0008521586,0.002682553,0.003650627,0.002422656,0.002004101],"category_scores_gemma":[0.09557428,0.0003731699,0.001401989,0.00433629,0.001461158,0.004971297,0.002280372,0.001883059,0.0006184575],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002934305,"about_ca_system_score_gemma":0.001496101,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005325634,"about_ca_topic_score_gemma":0.004377689,"domain_scores_codex":[0.9883591,0.006447144,0.001163545,0.001352025,0.002325891,0.0003522874],"domain_scores_gemma":[0.731989,0.2432473,0.004260239,0.0101009,0.008929883,0.001472779],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.007427089,0.002527541,0.1542324,0.001218345,0.001435785,0.0000986017,0.001125407,0.2053584,0.001824458,0.009530048,0.01071497,0.604507],"study_design_scores_gemma":[0.0003399783,0.001184068,0.03428017,0.00009129872,0.0003637489,0.0002124168,0.0004962939,0.9506857,0.002533445,0.008774553,0.0009651885,0.00007308983],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8420516,0.003655722,0.1466085,0.0007230413,0.0001573161,0.0003625962,0.001460969,0.001013066,0.003967242],"genre_scores_gemma":[0.8829701,0.0006365164,0.1109848,0.00007423364,0.00008328605,0.0002435583,0.003882512,0.0001999343,0.0009251632],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02083345,"threshold_uncertainty_score":0.1101791,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4482315763401465,"score_gpt":0.4587910225596661,"score_spread":0.0105594462195196,"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."}}