{"id":"W4386031629","doi":"10.1145/3617171","title":"<scp>StubCoder</scp> : Automated Generation and Repair of Stub Code for Mock Objects","year":2023,"lang":"en","type":"article","venue":"ACM Transactions on Software Engineering and Methodology","topic":"Software Testing and Debugging Techniques","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China; Hong Kong University of Science and Technology; Impact Fund; National Science Foundation","keywords":"Stub (electronics); Computer science; Unit testing; Regression testing; Leverage (statistics); Test case; Programming language; Software; Software development; Artificial intelligence; Engineering; Structural engineering; Machine learning; Software construction","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":[0.002052809,0.001622915,0.0005106226,0.00151957,0.0008248613,0.001252516,0.002431544,0.001682015,0.01165251],"category_scores_gemma":[0.008709342,0.0008926198,0.0008550024,0.0007322828,0.001895882,0.002370177,0.001873587,0.001249755,0.006893845],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006945487,"about_ca_system_score_gemma":0.001405525,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003678813,"about_ca_topic_score_gemma":0.005154542,"domain_scores_codex":[0.9985766,0.0003040163,0.00009884827,0.0002802886,0.0006152309,0.0001251019],"domain_scores_gemma":[0.9919104,0.002806657,0.0007797426,0.002972044,0.00130734,0.0002238337],"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":[0.0005238002,0.0004162525,0.01016419,0.00116461,0.0001384721,0.002313088,0.001301034,0.03573167,0.1269146,0.02501406,0.1695312,0.626787],"study_design_scores_gemma":[0.0002528722,0.0005934507,0.007126531,0.0002588009,0.00007812029,0.003621212,0.0001716456,0.437896,0.3254241,0.02051345,0.2038196,0.0002440792],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01529025,0.0001326504,0.7718596,0.0003282079,0.00007480434,0.0003653613,0.00129416,0.2052373,0.005417568],"genre_scores_gemma":[0.182428,0.0003031058,0.7517792,0.0004901771,0.00006828165,0.0004881213,0.008092695,0.04224616,0.01410426],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01165251,"threshold_uncertainty_score":0.0389815,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1225201681221641,"score_gpt":0.3394841656674571,"score_spread":0.216963997545293,"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."}}