{"id":"W4386511067","doi":"10.1016/j.cola.2023.101238","title":"A comparison of three solver-aided programming languages: <mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" altimg=\"si45.svg\" display=\"inline\" id=\"d1e1237\"> <mml:mi>α</mml:mi> </mml:math> Rby, ProB, and Rosette","year":2023,"lang":"en","type":"article","venue":"Journal of Computer Languages","topic":"Model-Driven Software Engineering Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Sherbrooke","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada; Fonds Québécois de la Recherche sur la Nature et les Technologies","keywords":"Solver; Computer science; Programming language; Domain (mathematical analysis); Exploit; Software; Premise; Constraint programming; Domain-specific language; Theoretical computer science; Mathematics; Stochastic programming; Mathematical optimization","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":[0.0035026,0.0008389318,0.0005685133,0.001085831,0.0005807304,0.003137731,0.002455652,0.001084542,0.01915259],"category_scores_gemma":[0.013127,0.0005214481,0.0008768979,0.001583416,0.0005830009,0.003512443,0.001971147,0.001674063,0.004016954],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007844039,"about_ca_system_score_gemma":0.002945166,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003838997,"about_ca_topic_score_gemma":0.007715908,"domain_scores_codex":[0.9976128,0.0008635788,0.0002170588,0.0002270425,0.0009207998,0.0001586443],"domain_scores_gemma":[0.9881818,0.007752226,0.0003747045,0.001839515,0.001564857,0.0002868728],"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":[0.005969987,0.001866733,0.008716254,0.003343468,0.000455315,0.000268964,0.001618681,0.08658402,0.02507822,0.1412113,0.09611857,0.6287684],"study_design_scores_gemma":[0.001494534,0.001506318,0.006901467,0.0006008563,0.0003647044,0.0006579628,0.001326581,0.622024,0.07725483,0.03781582,0.2497426,0.000310309],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1617691,0.001653337,0.6331807,0.002280716,0.0003937254,0.0006201642,0.007652235,0.09045661,0.1019934],"genre_scores_gemma":[0.3195052,0.001778675,0.6251295,0.0008404157,0.0000405745,0.0008013164,0.01336915,0.01302474,0.02551043],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01915259,"threshold_uncertainty_score":0.06407177,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02003936770226682,"score_gpt":0.2781056956162871,"score_spread":0.2580663279140202,"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."}}