{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004988289,0.001309076,0.001001173,0.006735808,0.0004632132,0.001781661,0.001627736,0.001169761,0.000851198],"category_scores_gemma":[0.04963757,0.0006997342,0.0006666296,0.002275832,0.0006745247,0.001731838,0.0011422,0.0008865767,0.0005273419],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008148798,"about_ca_system_score_gemma":0.001055403,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001588703,"about_ca_topic_score_gemma":0.003037476,"domain_scores_codex":[0.9916933,0.001944093,0.0008104275,0.001331998,0.00383145,0.0003886655],"domain_scores_gemma":[0.90964,0.0504823,0.01900459,0.008595759,0.01067179,0.001605623],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0007550867,0.0005701797,0.2599698,0.001123842,0.0003314913,0.003099248,0.003290041,0.02089336,0.1421252,0.003557708,0.006045376,0.5582386],"study_design_scores_gemma":[0.0001476652,0.001614946,0.239102,0.0005825029,0.0003573069,0.006215305,0.001910657,0.5216556,0.2038651,0.008275889,0.01584843,0.0004247213],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6312799,0.001324296,0.3395823,0.0005735476,0.00007744905,0.0004603048,0.001315486,0.02136363,0.004023146],"genre_scores_gemma":[0.8122084,0.0002762273,0.1839869,0.0001531501,0.00002327553,0.0002108694,0.001251605,0.0007636073,0.001125963],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006735808,"threshold_uncertainty_score":0.0263809,"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."}}