{"id":"W4376606818","doi":"10.1109/saner56733.2023.00071","title":"Combining Contexts from Multiple Sources for Documentation-Specific Code Example Generation","year":2023,"lang":"en","type":"article","venue":"","topic":"Software Engineering Research","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; Alberta Innovates; University of Calgary","keywords":"Documentation; Internal documentation; Computer science; Code (set theory); Programming language; Compiler; Source code; Software documentation; Unit testing; Redundant code; Code generation; Software engineering; Software; Software development; Operating system; Key (lock); Set (abstract data type); Software development process; Software construction","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":[0.002241348,0.001217892,0.0005389418,0.00166325,0.0004689877,0.001048478,0.001259192,0.001125895,0.002280135],"category_scores_gemma":[0.01870577,0.0005461057,0.0008415795,0.001016409,0.0004852664,0.002265033,0.002341475,0.001621685,0.001503392],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000428916,"about_ca_system_score_gemma":0.001150958,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001408538,"about_ca_topic_score_gemma":0.004824378,"domain_scores_codex":[0.9980978,0.0009376408,0.0001145942,0.0004394919,0.0003156928,0.00009475328],"domain_scores_gemma":[0.9910395,0.005479954,0.0003827633,0.001826968,0.00106337,0.0002074175],"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":[0.0007455731,0.0007126296,0.03914167,0.001657663,0.0001907312,0.001466584,0.002822186,0.03699181,0.026781,0.005197184,0.01744072,0.8668523],"study_design_scores_gemma":[0.0002555705,0.0007522217,0.01393038,0.0007018074,0.0002813822,0.001747644,0.001230506,0.8413417,0.05886582,0.02270104,0.05803912,0.0001527644],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3946874,0.002399927,0.5566152,0.001307983,0.0002980012,0.001101859,0.002300666,0.03162877,0.009660264],"genre_scores_gemma":[0.5234863,0.0004435192,0.4649392,0.0003845766,0.00006149514,0.0005858695,0.005044691,0.001798515,0.003255882],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002280135,"threshold_uncertainty_score":0.01185352,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08059542835924342,"score_gpt":0.3064540948118102,"score_spread":0.2258586664525667,"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."}}