{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003022718,0.0001537939,0.0001070698,0.00006415628,0.000308462,0.0003862383,0.0004153485,0.00007454477,0.000007271437],"category_scores_gemma":[0.004186609,0.0001248058,0.00001780838,0.0003110029,0.0001304795,0.001423741,0.000108879,0.0002622458,0.00001147222],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000104807,"about_ca_system_score_gemma":0.0001105332,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001651771,"about_ca_topic_score_gemma":0.00002009538,"domain_scores_codex":[0.9988508,0.00003585339,0.0001391219,0.0004545863,0.0002737198,0.0002459204],"domain_scores_gemma":[0.9978876,0.001047677,0.0001211108,0.0006128618,0.0002067539,0.0001240582],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00009877362,0.00104483,0.1822762,0.00007810337,0.00003573206,0.0005558779,0.02242975,0.00005914263,0.02498166,0.01153468,0.01467259,0.7422327],"study_design_scores_gemma":[0.005407287,0.007434058,0.03204218,0.0005166905,0.0002474444,0.01080286,0.001490532,0.3121645,0.1921679,0.03222683,0.3973705,0.008129275],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.05146594,0.00007893929,0.9411528,0.001370564,0.0002334117,0.0001495097,0.000001343304,0.005231553,0.0003159085],"genre_scores_gemma":[0.4803548,0.000005528542,0.5184958,0.0009974217,0.00006236153,0.00005180592,0.000002104636,0.000007611266,0.00002248626],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.7341034,"threshold_uncertainty_score":0.5089431,"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."}}