{"id":"W2102147011","doi":"10.1145/2635868.2635877","title":"Selection and presentation practices for code example summarization","year":2014,"lang":"en","type":"article","venue":"","topic":"Software Engineering Research","field":"Computer Science","cited_by":35,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; McGill University","keywords":"Automatic summarization; Computer science; Presentation (obstetrics); Code (set theory); Selection (genetic algorithm); Source code; Code review; Think aloud protocol; Information retrieval; Software; Natural language processing; Artificial intelligence; Static program analysis; Software development; Programming language; Human–computer interaction; Usability; Set (abstract data type)","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":[0.0003479445,0.00003540084,0.00003461261,0.00005761757,0.00006111291,0.0001515558,0.0001062691,0.00002224884,0.000003110664],"category_scores_gemma":[0.001571461,0.00003352819,0.000006452867,0.000151909,0.000005046882,0.000556766,0.00003753832,0.00003010796,0.000003281882],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001244158,"about_ca_system_score_gemma":0.0000132215,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001540472,"about_ca_topic_score_gemma":0.00005889759,"domain_scores_codex":[0.9995428,0.00002849048,0.00005732556,0.0001648381,0.0001112018,0.00009534606],"domain_scores_gemma":[0.9986303,0.001111076,0.00004456377,0.0001000993,0.00008391916,0.00003006723],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004342439,0.0001796312,0.163441,0.000310345,0.0000511401,3.808983e-7,0.001509083,0.006542817,0.03619353,0.4129251,0.01248125,0.3663223],"study_design_scores_gemma":[0.000200075,0.0001137062,0.02512489,0.000002607872,0.000001956161,0.00000285733,0.000004998133,0.9494303,0.008639235,0.001457434,0.01495969,0.00006217042],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02817646,0.000005830886,0.9709266,0.0003050512,0.00007451885,0.000158316,2.272782e-7,0.0001898253,0.0001632077],"genre_scores_gemma":[0.7961695,0.000004484044,0.2030425,0.00003483071,0.00006448455,0.00004332294,0.000005143893,0.000005607265,0.0006301154],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9428875,"threshold_uncertainty_score":0.1881299,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04724173993157055,"score_gpt":0.3281944050403541,"score_spread":0.2809526651087836,"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."}}