{"id":"W1995584399","doi":"10.1142/s0218194009004489","title":"TEMPORAL SOFTWARE CHANGE PREDICTION USING NEURAL NETWORKS","year":2009,"lang":"en","type":"article","venue":"International Journal of Software Engineering and Knowledge Engineering","topic":"Software Engineering Research","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Eclipse; Computer science; Software maintenance; Software; Software evolution; Dimension (graph theory); Plan (archaeology); Software development; Artificial neural network; Software engineering; Scale (ratio); Data science; Data mining; Artificial intelligence; Machine learning; Software construction; Operating system","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000495894,0.0003175248,0.0003342934,0.0008158667,0.00005764456,0.0002577541,0.0009693235,0.0001469728,0.000003567123],"category_scores_gemma":[0.00132592,0.000325985,0.0001541979,0.0004573586,0.00001687858,0.001239885,0.0001964135,0.000648815,0.000001780776],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002476962,"about_ca_system_score_gemma":0.00005979212,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005702993,"about_ca_topic_score_gemma":2.87609e-7,"domain_scores_codex":[0.998141,0.00002050726,0.0005664794,0.0002913717,0.0005394481,0.0004411812],"domain_scores_gemma":[0.9983399,0.0004648653,0.00015023,0.0002434661,0.0005140619,0.0002874948],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002465023,0.0001134311,0.02415118,0.00007099954,0.0002132946,0.0003303166,0.0008199026,0.899726,0.0007707279,0.0004424552,0.0001575872,0.07317942],"study_design_scores_gemma":[0.0005278997,0.0001427969,0.04813757,0.0003338523,0.00001487123,0.0008938243,0.000005155611,0.9483674,0.000153929,0.00001828595,0.001112584,0.0002918968],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.150182,0.003257252,0.8422801,0.00008657234,0.003632585,0.00009650509,0.000004757781,0.0004586212,0.000001577557],"genre_scores_gemma":[0.8852705,0.00009326568,0.1127859,0.0000304784,0.001761324,0.000005069666,0.000004102523,0.00003841681,0.00001097911],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7350884,"threshold_uncertainty_score":0.9999192,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02304885103198641,"score_gpt":0.2666174335153574,"score_spread":0.243568582483371,"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."}}