{"id":"W2104607014","doi":"10.1109/icsm.2012.6405278","title":"Relating requirements to implementation via topic analysis: Do topics extracted from requirements make sense to managers and developers?","year":2012,"lang":"en","type":"article","venue":"","topic":"Software Engineering Research","field":"Computer Science","cited_by":42,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Latent Dirichlet allocation; Computer science; Traceability; Documentation; Relevance (law); Perception; Topic model; Control (management); Requirements analysis; Requirements traceability; Requirements engineering; Requirements elicitation; Software engineering; Software; Information retrieval; Requirement; Artificial intelligence; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005635745,0.0001608741,0.0001791847,0.0004104146,0.0001151096,0.0002227833,0.0003288889,0.00005171989,0.0001192136],"category_scores_gemma":[0.0001930784,0.0001605848,0.00003952886,0.001302556,0.000007790296,0.0006204445,0.0005041889,0.0001011037,0.00007249301],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001873114,"about_ca_system_score_gemma":0.00001988215,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000453662,"about_ca_topic_score_gemma":0.0001166586,"domain_scores_codex":[0.9980793,0.00006372557,0.000337833,0.0004472329,0.000535658,0.0005362686],"domain_scores_gemma":[0.9988301,0.0001508819,0.00004925766,0.0005399294,0.00008897641,0.000340818],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000009136948,0.00004703258,0.6511285,0.00001884998,0.0005239249,0.00002333955,0.00677636,0.0002705802,0.007343689,0.001174963,0.0006705879,0.332013],"study_design_scores_gemma":[0.0003414915,0.0000505596,0.9900249,0.0000201231,0.00006453102,0.000002327634,0.0003716842,0.002406098,0.003099697,0.00009714132,0.003190974,0.0003304956],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5472599,0.00002979482,0.4513724,0.0004689125,0.0001968529,0.0002688814,0.000001899248,0.0001295508,0.0002718122],"genre_scores_gemma":[0.8082057,0.000002824969,0.1906334,0.0004324284,0.0000794171,0.00003022751,0.00001174272,0.00001059754,0.0005936867],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3388964,"threshold_uncertainty_score":0.6548458,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04333193516344207,"score_gpt":0.3415317457067007,"score_spread":0.2981998105432586,"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."}}