{"id":"W4104734","doi":"10.1007/978-3-642-39742-4_26","title":"A Multi-objective Genetic Algorithm for Generating Test Suites from Extended Finite State Machines","year":2013,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Software Testing and Debugging Techniques","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":false,"ca_institutions":"Carleton University","funders":"Consortium de Recherche et d’innovation en Aérospatiale au Québec","keywords":"Computer science; Test suite; Finite-state machine; Genetic algorithm; Similarity (geometry); Algorithm; Set (abstract data type); State (computer science); Test (biology); Suite; Test case; Artificial intelligence; Machine learning; Programming language","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":[0.001084044,0.001228133,0.001196775,0.001489639,0.0004903472,0.0006971241,0.001651771,0.0017432,0.002856675],"category_scores_gemma":[0.003432754,0.0006836291,0.001284839,0.001119744,0.0007360108,0.000638305,0.0009337185,0.001314583,0.0004038704],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001059008,"about_ca_system_score_gemma":0.001420803,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007023788,"about_ca_topic_score_gemma":0.006551438,"domain_scores_codex":[0.9994373,0.0001838457,0.00002636673,0.0001072482,0.0001852664,0.00005998015],"domain_scores_gemma":[0.9984226,0.001217399,0.00008248671,0.00006167714,0.0001759408,0.00003993021],"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":[0.00005917088,0.00009593178,0.0003738232,0.00006605063,0.00005773331,0.00008548695,0.0000465314,0.87449,0.002302828,0.00420664,0.0008358107,0.11738],"study_design_scores_gemma":[0.0000165811,0.0000250432,0.00006959585,0.000006523242,0.000009777391,0.00001249547,0.000003666113,0.9980258,0.0003628763,0.001324781,0.0001391891,0.000003676273],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02484555,0.0001995111,0.9708773,0.0000958964,0.00003787613,0.0001556646,0.00008584318,0.001229473,0.002472944],"genre_scores_gemma":[0.1980114,0.0001179855,0.7988884,0.00009747987,0.00002284705,0.0004559683,0.000308916,0.0002121968,0.001884829],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007023788,"threshold_uncertainty_score":0.01396579,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0205926823190309,"score_gpt":0.2616508539926293,"score_spread":0.2410581716735984,"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."}}