{"id":"W1494300071","doi":"10.1007/978-3-642-13054-0_4","title":"Extreme Product Line Engineering – Refactoring for Variability: A Test-Driven Approach","year":2010,"lang":"en","type":"book-chapter","venue":"Lecture notes in business information processing","topic":"Advanced Software Engineering Methodologies","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Code refactoring; Software product line; Eclipse; Software engineering; Computer science; Implementation; Domain engineering; Feature model; Product (mathematics); Software; Process (computing); Feature (linguistics); Domain analysis; Domain (mathematical analysis); Systems engineering; Software development; Engineering; Software construction; 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":[0.002546584,0.0009821207,0.0007330574,0.001047118,0.0003352328,0.001279074,0.002884722,0.001318001,0.001918878],"category_scores_gemma":[0.006510589,0.0006402517,0.001239491,0.0009255622,0.0009127267,0.001541873,0.001056049,0.001752309,0.0004668376],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004472414,"about_ca_system_score_gemma":0.0007257852,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007179299,"about_ca_topic_score_gemma":0.0008495771,"domain_scores_codex":[0.9974871,0.0007734,0.0001388354,0.0002646897,0.001185953,0.0001498877],"domain_scores_gemma":[0.9916621,0.004377843,0.0007275799,0.001914367,0.001200493,0.0001175645],"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":[0.0003277833,0.0005269027,0.003961201,0.0005621025,0.000282048,0.0008285012,0.0004375859,0.3477955,0.07581891,0.02900583,0.002019432,0.5384343],"study_design_scores_gemma":[0.00005520198,0.0004335964,0.001842344,0.00008684309,0.0001198563,0.000640681,0.00006748173,0.9138172,0.04958097,0.02926833,0.004027915,0.00005963209],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03041759,0.0001678257,0.9648455,0.0001273308,0.00001644757,0.00008624601,0.0000413267,0.001315682,0.002982104],"genre_scores_gemma":[0.5128403,0.0002288995,0.4825472,0.0001592191,0.00003153156,0.0001423992,0.0002877681,0.0004504323,0.00331227],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002884722,"threshold_uncertainty_score":0.01346773,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04312755450296103,"score_gpt":0.2581905754084423,"score_spread":0.2150630209054813,"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."}}