{"id":"W4411449687","doi":"10.1145/3715730","title":"Towards Diverse Program Transformations for Program Simplification","year":2025,"lang":"en","type":"article","venue":"Proceedings of the ACM on software engineering.","topic":"Software Engineering Research","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo; Concordia University","funders":"Engineering and Physical Sciences Research Council; Natural Sciences and Engineering Research Council of Canada","keywords":"Code refactoring; Program comprehension; Computer science; Source lines of code; Program transformation; Maintainability; Program slicing; Programming language; Heuristics; Software engineering; Set (abstract data type); Code (set theory); Static program analysis; Program analysis; Software; Source code; Software maintenance; Software quality; Software system; Software development; Operating system","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003432928,0.0002102608,0.0002061518,0.0003025095,0.0001350327,0.0001669491,0.003441374,0.0001058718,0.000001394426],"category_scores_gemma":[0.007989948,0.0001713928,0.0001837646,0.001306472,0.00004215079,0.0004015932,0.000533277,0.0002416038,0.000003660863],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001199922,"about_ca_system_score_gemma":0.00008387859,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003830884,"about_ca_topic_score_gemma":2.158746e-7,"domain_scores_codex":[0.9985519,0.000002833096,0.0003061983,0.0003431409,0.0004004074,0.0003955109],"domain_scores_gemma":[0.9982463,0.0003846003,0.00008100236,0.0007390422,0.0004738746,0.00007520436],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.00007526583,0.001127891,0.01319052,0.003063519,0.0002999183,5.040295e-7,0.001364675,0.005959195,0.002506872,0.1229915,0.02759247,0.8218277],"study_design_scores_gemma":[0.004300125,0.002296645,0.2860336,0.001898392,0.0002069417,0.00002084682,0.0001723164,0.2313979,0.1869912,0.0157472,0.2689019,0.002032923],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1806684,0.0002369457,0.7960631,0.005402125,0.001609351,0.007965228,0.00005199503,0.007654848,0.0003479662],"genre_scores_gemma":[0.4193683,0.00001156229,0.5783073,0.00006678321,0.0000537938,0.001999367,0.000004764119,0.00003051395,0.0001577169],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8197947,"threshold_uncertainty_score":0.956529,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01895505048035933,"score_gpt":0.2946897396904545,"score_spread":0.2757346892100952,"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."}}