{"id":"W2943138790","doi":"10.5539/cis.v12n2p138","title":"Towards Efficient Tracing in Software Product Lines: Research Methodology","year":2019,"lang":"en","type":"article","venue":"Computer and Information Science","topic":"Advanced Software Engineering Methodologies","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Tracing; Computer science; Reuse; Domain engineering; Software; Time to market; Return on investment; TRACE (psycholinguistics); Risk analysis (engineering); Product (mathematics); Software development; New product development; Domain (mathematical analysis); Software engineering; Component-based software engineering; Production (economics); Marketing; Business","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008199683,0.0001154067,0.0001949053,0.0009958615,0.0001571406,0.0003027999,0.001106515,0.00004210661,0.000002163866],"category_scores_gemma":[0.002012509,0.00009895024,0.00002034496,0.002355513,0.0002501726,0.004396922,0.0008876774,0.0002988989,0.00004902638],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009524526,"about_ca_system_score_gemma":0.0002176624,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001146291,"about_ca_topic_score_gemma":3.880317e-7,"domain_scores_codex":[0.9979901,0.0002131611,0.0003498004,0.0003828902,0.0005935176,0.000470506],"domain_scores_gemma":[0.9979806,0.0009648548,0.00007065266,0.0005351723,0.0003629069,0.00008581518],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000005576099,0.00001366621,0.0007497399,0.00008219478,0.000001475812,0.000001555707,0.008631466,0.3898051,0.0004522009,0.02794449,0.00003059833,0.5722819],"study_design_scores_gemma":[0.0005533511,0.000219584,0.06016597,0.0001170359,8.617163e-7,0.0000779806,0.0002918876,0.922639,0.006843844,0.005943153,0.002736849,0.0004104816],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.08899701,0.00007311233,0.9091002,0.0002236251,0.0009062693,0.0002517676,4.670444e-7,0.0001914341,0.0002560749],"genre_scores_gemma":[0.2285259,0.00001612603,0.7712615,0.000143426,0.00003321931,0.000009652586,5.719501e-7,0.000002396413,0.000007225114],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.5718715,"threshold_uncertainty_score":0.4035074,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1194630494290103,"score_gpt":0.3865855830130413,"score_spread":0.267122533584031,"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."}}