{"id":"W1989181588","doi":"10.1145/1852786.1852841","title":"Evaluation of optimized staffing for feature development and bug fixing","year":2010,"lang":"en","type":"article","venue":"","topic":"Software Engineering Research","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Staffing; Feature (linguistics); Productivity; Computer science; Empirical research; Software bug; Software engineering; Software; Operating system; Mathematics; Statistics; Management","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":[0.01113121,0.000614474,0.0006492698,0.001368242,0.0003282248,0.0008664061,0.001220529,0.0006771384,0.001540896],"category_scores_gemma":[0.07959281,0.0003669359,0.0004039226,0.001303527,0.0008190356,0.001379041,0.000506044,0.0007244353,0.0002405392],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002752667,"about_ca_system_score_gemma":0.002055745,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006230361,"about_ca_topic_score_gemma":0.004977365,"domain_scores_codex":[0.9912519,0.00483089,0.0006294662,0.0008354236,0.002073266,0.0003790883],"domain_scores_gemma":[0.8883442,0.08577577,0.01162962,0.006236985,0.006189995,0.001823461],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.01802758,0.004926341,0.1157334,0.0008897223,0.0006693443,0.0001448053,0.001066314,0.3579701,0.01154251,0.004548918,0.003101551,0.4813795],"study_design_scores_gemma":[0.002069766,0.02330488,0.2126408,0.0001207562,0.0004833564,0.0001974332,0.0007178739,0.7407752,0.0127645,0.003585865,0.003212655,0.000126983],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.982915,0.0004328873,0.0140896,0.00009609838,0.000023692,0.0001476879,0.0001714162,0.000395298,0.001728275],"genre_scores_gemma":[0.9895436,0.00006590391,0.009647558,0.00001936341,0.000009718361,0.00008539057,0.0002304,0.00003670138,0.0003614632],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01113121,"threshold_uncertainty_score":0.05886817,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03652472059376993,"score_gpt":0.3111011085496981,"score_spread":0.2745763879559281,"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."}}