{"id":"W2024941318","doi":"10.1145/505532.505551","title":"Generative techniques for product lines","year":2001,"lang":"en","type":"article","venue":"ACM SIGSOFT Software Engineering Notes","topic":"Advanced Software Engineering Methodologies","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Software product line; Reuse; Computer science; Product (mathematics); Software engineering; Domain (mathematical analysis); Software; Product line; Quality (philosophy); Domain engineering; Generative grammar; Feature model; Order (exchange); Domain analysis; Software development; Engineering; Software construction; Programming language; Artificial intelligence; Manufacturing engineering; Business","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":["metaresearch","metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005130425,0.0004458341,0.0004233043,0.0002674258,0.0001489107,0.0001317904,0.001672277,0.0001368538,0.00000408333],"category_scores_gemma":[0.2479459,0.0004371683,0.0001525752,0.0006420495,0.00004075365,0.0007437177,0.000489299,0.0002634375,0.00001069695],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009041299,"about_ca_system_score_gemma":0.00005693802,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005670321,"about_ca_topic_score_gemma":0.000001136486,"domain_scores_codex":[0.9979002,0.00003721254,0.0003770697,0.0007719754,0.0002501989,0.0006633105],"domain_scores_gemma":[0.9480011,0.04975299,0.0001188597,0.001669187,0.000309629,0.0001482082],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00007122265,0.0002468912,0.0464786,0.0007044447,0.0002801514,0.0001452859,0.001788401,0.1846902,0.08194422,0.01492851,0.003413591,0.6653085],"study_design_scores_gemma":[0.001114381,0.0007904405,0.0228392,0.0004290446,0.0000716894,0.0003748803,0.00001918159,0.007064209,0.8427517,0.01244599,0.1082337,0.003865586],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.005850647,0.001044239,0.9845559,0.0007013341,0.0008700448,0.0005543423,0.00001150331,0.0064105,0.000001486118],"genre_scores_gemma":[0.07592585,0.00008792092,0.9226664,0.0001456451,0.0005712693,0.0004029798,0.00001520019,0.000078776,0.0001059537],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.7608075,"threshold_uncertainty_score":0.999808,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04821734716399782,"score_gpt":0.3023470537692871,"score_spread":0.2541297066052893,"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."}}