{"id":"W3082254096","doi":"","title":"Using Deep Learning Classifiers to Identify Candidate Classes for Unit Testing in Object-Oriented Systems.","year":2020,"lang":"en","type":"article","venue":"Software Engineering and Knowledge Engineering","topic":"Software Testing and Debugging Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université du Québec à Trois-Rivières; Université du Québec","funders":"","keywords":"Computer science; Artificial intelligence; Deep learning; Unit testing; Object (grammar); Machine learning; Unit (ring theory); Pattern recognition (psychology); Mathematics; Programming language; Software","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":[0.001416323,0.0008488699,0.0005993368,0.001813087,0.0004182333,0.001018225,0.001450537,0.001406575,0.00150117],"category_scores_gemma":[0.007177928,0.0003396204,0.0005530635,0.0007136362,0.0004072076,0.001672573,0.0007789948,0.001390065,0.0005517777],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009461507,"about_ca_system_score_gemma":0.001019754,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01024974,"about_ca_topic_score_gemma":0.01415878,"domain_scores_codex":[0.9991719,0.0002593773,0.00006391454,0.0001704729,0.0001907079,0.0001436545],"domain_scores_gemma":[0.992862,0.005075764,0.0005253181,0.0003697936,0.0009185025,0.0002486183],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001096351,0.001000586,0.06483196,0.0002903788,0.0002372454,0.000378825,0.0002362015,0.2110993,0.01484817,0.00534192,0.01524983,0.6853892],"study_design_scores_gemma":[0.00001880186,0.00005147974,0.001600995,0.00001771044,0.00002155007,0.00002395775,0.00002909968,0.9915358,0.003210918,0.003037296,0.0004477612,0.000004590077],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.6547124,0.002199884,0.3279707,0.001387492,0.0001803936,0.0001659175,0.0008489442,0.00584077,0.006693434],"genre_scores_gemma":[0.9410173,0.0001225278,0.05563147,0.0001901848,0.0000340104,0.00005536831,0.0009925429,0.000105881,0.00185077],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.01024974,"threshold_uncertainty_score":0.02038014,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04895468532458588,"score_gpt":0.2947629974905722,"score_spread":0.2458083121659863,"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."}}