{"id":"W2920929439","doi":"10.1007/s10664-019-09690-0","title":"Extracting and studying the Logging-Code-Issue- Introducing changes in Java-based large-scale open source software systems","year":2019,"lang":"en","type":"article","venue":"Empirical Software Engineering","topic":"Software System Performance and Reliability","field":"Computer Science","cited_by":35,"is_retracted":false,"has_abstract":false,"ca_institutions":"York University","funders":"Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Logging; Debugging; Java; Source code; Correctness; Code (set theory); Software; Code review; Database; Software engineering; Static program analysis; Software development; Programming language","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001537277,0.000256967,0.0002238767,0.003083107,0.0004078207,0.0008600295,0.0005377444,0.0004135108,0.0005152844],"category_scores_gemma":[0.02601665,0.0002537361,0.0003498076,0.002451094,0.0004258462,0.001599652,0.0006582613,0.0008398867,0.0001969667],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005201931,"about_ca_system_score_gemma":0.0007670968,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004612684,"about_ca_topic_score_gemma":0.009563486,"domain_scores_codex":[0.997993,0.0002764604,0.0002313712,0.0005036036,0.0008504169,0.0001451765],"domain_scores_gemma":[0.9607843,0.01991148,0.009205152,0.003295519,0.006087775,0.0007157883],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001531164,0.000415799,0.8352122,0.0003303494,0.00008573946,0.0004200547,0.002069513,0.003776558,0.01434854,0.0009874637,0.001198218,0.1410024],"study_design_scores_gemma":[0.000009413896,0.0001076905,0.9443297,0.00003807294,0.00006669206,0.000314685,0.0008125183,0.04309295,0.008274079,0.001046361,0.001880934,0.00002687707],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9926733,0.0001221937,0.005954134,0.00005612868,0.00001435255,0.00002951346,0.0003667532,0.0002528747,0.0005307322],"genre_scores_gemma":[0.992841,0.00008171848,0.00570202,0.00001617454,0.00001290722,0.00001782464,0.000875867,0.0000786337,0.0003739176],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004612684,"threshold_uncertainty_score":0.009171665,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01834580222570305,"score_gpt":0.2655126594059878,"score_spread":0.2471668571802848,"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."}}