{"id":"W2057619340","doi":"10.1109/csmr-wcre.2014.6747182","title":"Examining the relationship between topic model similarity and software maintenance","year":2014,"lang":"en","type":"article","venue":"","topic":"Software Engineering Research","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Source code; Software maintenance; Context (archaeology); Similarity (geometry); Metric (unit); Software; Software development; Software metric; Code (set theory); Software engineering; Relation (database); KPI-driven code analysis; Static program analysis; Code review; Software quality; Data mining; Programming language; Artificial intelligence; Engineering","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":[0.0139614,0.0006202334,0.0009497367,0.00484375,0.001229495,0.004036891,0.0009837112,0.001836995,0.00242471],"category_scores_gemma":[0.178578,0.0004283765,0.001055285,0.006892826,0.001132032,0.005197254,0.001974837,0.002298559,0.0004020032],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001422569,"about_ca_system_score_gemma":0.0008239881,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005581783,"about_ca_topic_score_gemma":0.004294408,"domain_scores_codex":[0.9924523,0.004728416,0.0004452152,0.001037235,0.0009199398,0.0004168871],"domain_scores_gemma":[0.6454638,0.3177373,0.02005026,0.006688576,0.007067866,0.002992068],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0007437808,0.0004346673,0.92334,0.0002507582,0.0008499527,0.0002177183,0.00352298,0.01373527,0.001069817,0.008330196,0.001203122,0.04630169],"study_design_scores_gemma":[0.00007224819,0.0008625467,0.7226812,0.00009975061,0.0006322598,0.0009787441,0.004308121,0.235303,0.0009192434,0.03235149,0.001667482,0.0001240574],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9479552,0.001610879,0.04474133,0.001070903,0.00004386844,0.00009361536,0.0002686796,0.0001658898,0.004049612],"genre_scores_gemma":[0.9950626,0.0001754218,0.004118999,0.00003011979,0.00003576991,0.00004062658,0.0003011892,0.00001893655,0.0002162147],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0139614,"threshold_uncertainty_score":0.07383585,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0911580655639271,"score_gpt":0.2850683884987484,"score_spread":0.1939103229348214,"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."}}