{"id":"W4406421069","doi":"10.1007/s10489-024-06087-5","title":"Cross-project defect prediction based on autoencoder with dynamic adversarial adaptation","year":2025,"lang":"en","type":"article","venue":"Applied Intelligence","topic":"Software Engineering Research","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":false,"ca_institutions":"York University","funders":"National Natural Science Foundation of China","keywords":"Computer science; Autoencoder; Adversarial system; Adaptation (eye); Artificial intelligence; Machine learning; Artificial neural network; Neuroscience","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":[0.001306871,0.0009427813,0.001011984,0.0007922408,0.0002712242,0.0005122733,0.001206559,0.001071448,0.001188792],"category_scores_gemma":[0.003122346,0.0003332381,0.000558566,0.000584012,0.0005211095,0.001149679,0.001214919,0.001283308,0.0004169899],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004170275,"about_ca_system_score_gemma":0.0007086853,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003565435,"about_ca_topic_score_gemma":0.003860487,"domain_scores_codex":[0.9993235,0.0001159394,0.00003342481,0.0002044686,0.0002286632,0.0000940383],"domain_scores_gemma":[0.9982057,0.0006565746,0.0001794179,0.0002613784,0.0006101922,0.00008669979],"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":[0.0002963902,0.0002301945,0.00483419,0.00006715859,0.0001340756,0.000217885,0.00005391262,0.6932244,0.01021771,0.002761575,0.003275431,0.284687],"study_design_scores_gemma":[0.000001950416,0.00001663501,0.0003069,0.000002339229,0.000006406001,0.00002220536,0.000002347296,0.998157,0.0009884448,0.0004166126,0.00007583372,0.000003372855],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07699007,0.0004554461,0.9194866,0.000236148,0.0001160718,0.00004428994,0.00007662066,0.001212256,0.001382448],"genre_scores_gemma":[0.9005871,0.0002300872,0.09458025,0.0001749405,0.00006485583,0.00006310598,0.0003308433,0.0001017299,0.003867039],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003565435,"threshold_uncertainty_score":0.007089376,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0151509708269484,"score_gpt":0.2902956417986149,"score_spread":0.2751446709716665,"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."}}