{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01376238,0.001759803,0.001064896,0.005239498,0.001243458,0.006219992,0.003618789,0.002644201,0.004685205],"category_scores_gemma":[0.02557905,0.001829267,0.001976219,0.004967753,0.00404047,0.00784546,0.003057777,0.002533766,0.001147551],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003883244,"about_ca_system_score_gemma":0.007276498,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002712017,"about_ca_topic_score_gemma":0.002340585,"domain_scores_codex":[0.9883391,0.007592933,0.0007507296,0.001470783,0.001417306,0.0004290666],"domain_scores_gemma":[0.9756401,0.01729082,0.002193343,0.001904917,0.002558525,0.0004123623],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001020757,0.001146643,0.00702238,0.001943005,0.0001296262,0.0003939737,0.002256111,0.107503,0.004438718,0.6465829,0.001197738,0.2272838],"study_design_scores_gemma":[0.00009539678,0.0006464235,0.001626003,0.001025304,0.0001251053,0.0004550463,0.003082651,0.6711599,0.007803802,0.2907799,0.02310856,0.00009187175],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004838801,0.0003546458,0.9918985,0.0003760661,0.00001184462,0.0003674284,0.00003484781,0.00007737948,0.002040388],"genre_scores_gemma":[0.08159718,0.001219659,0.9146671,0.00009716154,0.00003089541,0.0008774867,0.0001072966,0.00005215692,0.001351066],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01376238,"threshold_uncertainty_score":0.07278329,"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."}}