{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001145399,0.0006107556,0.0005125101,0.001786651,0.0003518523,0.0007866982,0.0008976746,0.0008509897,0.0007826907],"category_scores_gemma":[0.006621259,0.0003288836,0.0005319561,0.001386006,0.0003076756,0.001205404,0.0004583937,0.0008824058,0.0001739815],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001297341,"about_ca_system_score_gemma":0.0005598939,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02759438,"about_ca_topic_score_gemma":0.02190315,"domain_scores_codex":[0.9993706,0.0001506823,0.0000476619,0.0002014372,0.0001405333,0.00008909003],"domain_scores_gemma":[0.9965761,0.002119984,0.0004707184,0.000150866,0.0006051561,0.00007710995],"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":[0.0003038716,0.0001738258,0.02299769,0.00006651852,0.0001070332,0.0001605008,0.00007049583,0.8253818,0.001328276,0.0009430996,0.001260131,0.1472068],"study_design_scores_gemma":[0.000001700108,0.000006527018,0.0009164119,0.000001929059,0.000004549194,0.000005528199,0.000004047376,0.9984179,0.0001707988,0.0004176527,0.00005087367,0.000002123261],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6086302,0.001333615,0.3823237,0.0008181725,0.0001352968,0.00009734138,0.0008641608,0.002144965,0.003652543],"genre_scores_gemma":[0.9728622,0.0001915095,0.02546775,0.00005098597,0.00003846098,0.00004062344,0.000524827,0.00001910922,0.0008043729],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02759438,"threshold_uncertainty_score":0.05486757,"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."}}