{"id":"W2972465897","doi":"10.1007/s10009-019-00530-6","title":"Diversity of graph models and graph generators in mutation testing","year":2019,"lang":"en","type":"article","venue":"International Journal on Software Tools for Technology Transfer","topic":"Software Testing and Debugging Techniques","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Emberi Eroforrások Minisztériuma; Natural Sciences and Engineering Research Council of Canada; Magyar Tudományos Akadémia; Budapesti Műszaki és Gazdaságtudományi Egyetem","keywords":"Computer science; Test suite; Predicate abstraction; Random testing; Graph; Model-based testing; Theoretical computer science; Predicate (mathematical logic); Software quality; Programming language; Test case; Software; Software engineering; Model checking; Machine learning; Software development","routes":{"ca_aff":true,"ca_fund":true,"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":[0.00855693,0.000722795,0.0007449431,0.003333987,0.0007154479,0.001742334,0.001395666,0.001683874,0.000785283],"category_scores_gemma":[0.05344065,0.0006858926,0.0009096464,0.001262065,0.002732038,0.003395042,0.00236446,0.00132902,0.0001080064],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001397137,"about_ca_system_score_gemma":0.0007958084,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0013617,"about_ca_topic_score_gemma":0.00152694,"domain_scores_codex":[0.989608,0.006253354,0.0003626863,0.001012531,0.00237291,0.0003906434],"domain_scores_gemma":[0.9092877,0.07603114,0.004663319,0.006129157,0.002904227,0.0009844657],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000517319,0.0002756941,0.0234336,0.0001404509,0.0001119428,0.0004300984,0.0005780072,0.8733724,0.013036,0.03121748,0.00045872,0.0564284],"study_design_scores_gemma":[0.00003671,0.0002755478,0.00188143,0.00002672753,0.0000320951,0.0002607667,0.0001038385,0.9626543,0.007796283,0.02644657,0.0004625515,0.00002313869],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6908224,0.0002752193,0.3058698,0.0003063079,0.00002009943,0.0001313017,0.0001171492,0.0005555672,0.001902111],"genre_scores_gemma":[0.9522921,0.00005983265,0.04706463,0.00003535986,0.00000872948,0.00005630822,0.0001885261,0.000107165,0.0001872553],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00855693,"threshold_uncertainty_score":0.04525393,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03993430915760716,"score_gpt":0.2690731531534225,"score_spread":0.2291388439958153,"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."}}