{"id":"W3096162789","doi":"10.1007/s10664-020-09874-z","title":"A feature location approach for mapping application features extracted from crowd-based screencasts to source code","year":2020,"lang":"en","type":"article","venue":"Empirical Software Engineering","topic":"Software Engineering Research","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"Queen's University; Concordia University","funders":"","keywords":"Computer science; Source code; Codebase; Set (abstract data type); Program comprehension; Software; Workflow; Code (set theory); Information retrieval; Multimedia; World Wide Web; Human–computer interaction; Database; Software system; Programming language","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000439507,0.0007582382,0.0006053616,0.004282775,0.0005083641,0.0009243701,0.0007670635,0.0009168015,0.002441106],"category_scores_gemma":[0.00300922,0.0002507025,0.0006225725,0.002637594,0.0003323415,0.001093153,0.001577083,0.0005985278,0.002072169],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003866076,"about_ca_system_score_gemma":0.0007569639,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01003372,"about_ca_topic_score_gemma":0.01416969,"domain_scores_codex":[0.9994062,0.00008362423,0.00002417336,0.0002121068,0.0001909962,0.00008285067],"domain_scores_gemma":[0.9987962,0.0003151611,0.0001440806,0.0002256255,0.0004443568,0.00007449171],"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":[0.0007864829,0.0005829409,0.02861005,0.0003082387,0.0002072353,0.0008516751,0.001614259,0.02314644,0.09742752,0.005535602,0.01576177,0.8251678],"study_design_scores_gemma":[0.00009001906,0.0004722533,0.06280711,0.00007683021,0.0002164257,0.000962052,0.001968207,0.8543856,0.04650876,0.01201093,0.02033143,0.0001704748],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.111839,0.0003369836,0.8720528,0.0001931332,0.0001304724,0.0002244841,0.002308716,0.007765115,0.005149411],"genre_scores_gemma":[0.6687675,0.0001803328,0.32152,0.00006297245,0.00008685599,0.0002852006,0.002785392,0.0002760505,0.006035622],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01003372,"threshold_uncertainty_score":0.01995063,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03464601524693783,"score_gpt":0.2754876655325143,"score_spread":0.2408416502855765,"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."}}