{"id":"W2807786268","doi":"10.24963/ijcai.2018/399","title":"Cutting the Software Building Efforts in Continuous Integration by Semi-Supervised Online AUC Optimization","year":2018,"lang":"en","type":"article","venue":"","topic":"Software Engineering Research","field":"Computer Science","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"Novelis (Canada)","funders":"National Key Research and Development Program of China; National Natural Science Foundation of China","keywords":"Computer science; Outcome (game theory); Software; Event (particle physics); Code (set theory); Resource (disambiguation); Software engineering; Data science; Machine learning; Artificial intelligence; Set (abstract data type)","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":[],"consensus_categories":[],"category_scores_codex":[0.0005604718,0.0001279366,0.0001212003,0.0001360941,0.00012499,0.0002190104,0.0007266867,0.0000685367,0.00003535408],"category_scores_gemma":[0.001332251,0.00009269617,0.00002942193,0.0007576269,0.00004702379,0.0004611744,0.0002384418,0.0002123596,0.00001090949],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009694504,"about_ca_system_score_gemma":0.00004610464,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001517852,"about_ca_topic_score_gemma":0.00003384516,"domain_scores_codex":[0.9987273,0.00005951885,0.0002411982,0.0003257133,0.0003198061,0.0003264299],"domain_scores_gemma":[0.9987053,0.0005859296,0.00004184177,0.0004371064,0.00017388,0.00005598492],"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":[0.00003347765,0.0004383495,0.07689812,0.00007755981,0.00005953303,0.00003842675,0.006843239,0.2644929,0.01244044,0.005619627,0.01977863,0.6132797],"study_design_scores_gemma":[0.0002317675,0.00006126184,0.001598389,0.00005761555,0.000001403498,0.000007442089,0.00004574459,0.9916638,0.005640526,0.00008993939,0.0004698105,0.0001323302],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1197347,0.00005090971,0.8787968,0.0005368619,0.0002116692,0.0002085751,0.000001683501,0.0004050788,0.0000537007],"genre_scores_gemma":[0.6413502,0.00000608078,0.3581295,0.0001881397,0.0001001603,0.00001671364,0.00001348145,0.00001471303,0.0001810334],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7271709,"threshold_uncertainty_score":0.378004,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01235004194634493,"score_gpt":0.2650541921919011,"score_spread":0.2527041502455562,"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."}}