{"id":"W3105178636","doi":"10.1145/3368089.3409737","title":"Mining assumptions for software components using machine learning","year":2020,"lang":"en","type":"article","venue":"","topic":"Software Testing and Debugging Techniques","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada; Fonds National de la Recherche Luxembourg; European Commission; H2020 European Research Council; Université du Luxembourg","keywords":"Computer science; Component (thermodynamics); Set (abstract data type); Machine learning; Test case; Software; Decision tree; Process (computing); Tree (set theory); Artificial intelligence; Component-based software engineering; Software system; Data mining; Programming language; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"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":[],"consensus_categories":[],"category_scores_codex":[0.0001605283,0.0001129136,0.0001385865,0.00005556224,0.0002780269,0.0001282837,0.0004405143,0.00004106496,0.000008582319],"category_scores_gemma":[0.0007685176,0.0001086854,0.00006395984,0.0002214854,0.00001654186,0.0002089485,0.0002276729,0.0001105172,0.000009693519],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002125843,"about_ca_system_score_gemma":0.00002511766,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006437967,"about_ca_topic_score_gemma":0.000001404808,"domain_scores_codex":[0.9991356,0.00003500036,0.0001653424,0.0003057444,0.0001392113,0.0002190815],"domain_scores_gemma":[0.9991723,0.0003967387,0.00007082561,0.0001828078,0.00006834446,0.0001090199],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00006135956,0.0003254694,0.7080017,0.0004411698,0.0001886291,0.00007043831,0.007612887,0.01445829,0.0188802,0.01199951,0.05018175,0.1877786],"study_design_scores_gemma":[0.0001761733,0.00008496084,0.0004947719,0.00003001681,0.000006598956,0.00001152509,0.000003775369,0.9937935,0.000704459,0.001822137,0.002705482,0.0001665334],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01611999,0.00005036476,0.9685885,0.0007881653,0.00007787167,0.0001255824,0.000002077071,0.01418197,0.00006550752],"genre_scores_gemma":[0.2398106,8.469775e-7,0.7592965,0.0007718577,0.00004844541,0.000009133564,0.000006982232,0.00001204053,0.00004361804],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9793352,"threshold_uncertainty_score":0.4432062,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1261285273810029,"score_gpt":0.3063556652720434,"score_spread":0.1802271378910405,"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."}}