{"id":"W1991242370","doi":"10.1109/icst.2013.45","title":"Automated Detection of Test Fixture Strategies and Smells","year":2013,"lang":"en","type":"article","venue":"","topic":"Software Engineering Research","field":"Computer Science","cited_by":79,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Fixture; Code refactoring; Code smell; Computer science; Test fixture; Test (biology); Code (set theory); Reliability engineering; Software engineering; Code coverage; Test Management Approach; Set (abstract data type); Engineering; Software quality; Software; Programming language; Software system; Software development; Software construction","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":[0.00006058991,0.00004372169,0.00005118505,0.00005983467,0.00001695842,0.0001446956,0.0001693143,0.00003214195,0.00002070871],"category_scores_gemma":[0.0001604569,0.00003485267,0.000008342317,0.0001996537,0.00002003571,0.0004919437,0.00007257597,0.00005303877,0.000029748],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000006927935,"about_ca_system_score_gemma":0.00002028198,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000151285,"about_ca_topic_score_gemma":0.000003906344,"domain_scores_codex":[0.9996076,0.000007389964,0.0000652777,0.0001055765,0.0001126779,0.0001014836],"domain_scores_gemma":[0.9993758,0.0003238059,0.00001229566,0.000175684,0.00007682839,0.00003560528],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000001122187,0.00007425708,0.01354546,0.0001514082,0.00002380129,0.000005554425,0.000678317,0.0007983739,0.8928615,0.004845807,0.002111227,0.08490318],"study_design_scores_gemma":[0.0001157458,0.0001164991,0.4276559,0.00001072039,7.997322e-7,0.00001166228,0.00005831098,0.4658432,0.1051483,0.0008777528,0.00006895926,0.0000921451],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6921569,0.00006689898,0.3061076,0.0001336952,0.00006396134,0.0001143324,2.587819e-7,0.001013268,0.0003431393],"genre_scores_gemma":[0.9880296,0.000003175159,0.01182977,0.000007587693,0.00000737002,0.00000608109,1.108209e-7,0.000002811485,0.000113466],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7877132,"threshold_uncertainty_score":0.1421251,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008291020522666195,"score_gpt":0.2361008565994002,"score_spread":0.227809836076734,"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."}}