{"id":"W4378676770","doi":"10.1109/icstw58534.2023.00060","title":"Analysis of mutation operators for FSM testing","year":2023,"lang":"en","type":"article","venue":"","topic":"Software Testing and Debugging Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Mutation testing; Mutation; Computer science; Operator (biology); Finite-state machine; Software testing; Software fault tolerance; Process (computing); Software; Set (abstract data type); Fault (geology); Mutant; Theoretical computer science; Algorithm; Programming language; Artificial intelligence; Biology; Genetics","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":[0.004839938,0.0008944683,0.0005765493,0.002592941,0.0005035583,0.0006674153,0.0007875428,0.0009801016,0.00115068],"category_scores_gemma":[0.03541686,0.0002323815,0.000932497,0.0009541411,0.001202079,0.00108009,0.0005864305,0.0009003857,0.000125368],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001385347,"about_ca_system_score_gemma":0.001167302,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001635141,"about_ca_topic_score_gemma":0.001230987,"domain_scores_codex":[0.9931192,0.00219116,0.0003304647,0.0005306749,0.003485531,0.0003430364],"domain_scores_gemma":[0.952477,0.04031556,0.001950783,0.001803469,0.003031953,0.0004211793],"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":[0.0007742068,0.0005356462,0.02227019,0.0004489483,0.0002075198,0.0007777564,0.0003633784,0.626242,0.09550671,0.03879161,0.001378244,0.2127037],"study_design_scores_gemma":[0.00003117598,0.0002152243,0.002003252,0.0000207636,0.00004157136,0.0002293781,0.00003392367,0.9681962,0.02005726,0.008546317,0.0006071738,0.00001771559],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5056775,0.0005544146,0.4873362,0.0002527418,0.00004125464,0.000327297,0.0002913641,0.002218717,0.003300517],"genre_scores_gemma":[0.8540689,0.0001039001,0.1445044,0.0000625488,0.00001507906,0.0001656139,0.0003274105,0.0002086466,0.0005434912],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004839938,"threshold_uncertainty_score":0.02559638,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06239286997447335,"score_gpt":0.3228523354153964,"score_spread":0.2604594654409231,"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."}}