{"id":"W2104838677","doi":"10.1002/spe.1017","title":"Grammar‐based test generation with YouGen","year":2010,"lang":"en","type":"article","venue":"Software Practice and Experience","topic":"Software Testing and Debugging Techniques","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Computer science; Parsing; Grammar; Natural language processing; Compiler; Rule-based machine translation; Programming language; Artificial intelligence; Generator (circuit theory); Context (archaeology); Linguistics; Power (physics)","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":[0.003817294,0.001073451,0.0006233325,0.001677669,0.0003251608,0.001664564,0.001874219,0.00129288,0.012419],"category_scores_gemma":[0.01465471,0.0008426496,0.001257959,0.0007897143,0.001530964,0.002557138,0.003139121,0.001498941,0.003737262],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005846895,"about_ca_system_score_gemma":0.0009894656,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008262607,"about_ca_topic_score_gemma":0.000977336,"domain_scores_codex":[0.9960725,0.001605796,0.0003177225,0.0005254254,0.001195175,0.0002833322],"domain_scores_gemma":[0.9897114,0.006586017,0.0004634949,0.0019922,0.00106897,0.0001780368],"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":[0.000898294,0.0005167669,0.009568476,0.001288806,0.0002707103,0.002692516,0.001588453,0.1417771,0.02415307,0.1275821,0.06982379,0.6198398],"study_design_scores_gemma":[0.0004317187,0.0003852459,0.001594142,0.0003337938,0.0001198098,0.001885608,0.000302278,0.6760918,0.08046257,0.118291,0.1199285,0.000173455],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01981211,0.0002365904,0.8868251,0.0003430107,0.0001288793,0.0003167787,0.0006023317,0.08440581,0.007329328],"genre_scores_gemma":[0.2860681,0.0002618899,0.6842024,0.0005747865,0.00005542858,0.0006494846,0.004531194,0.01551799,0.008138744],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.012419,"threshold_uncertainty_score":0.04154575,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01713707136432013,"score_gpt":0.2785418352168769,"score_spread":0.2614047638525568,"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."}}