{"id":"W3025537827","doi":"10.1109/tse.2021.3087087","title":"Generating Unit Tests for Documentation","year":2021,"lang":"en","type":"preprint","venue":"IEEE Transactions on Software Engineering","topic":"Software Engineering Research","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Documentation; Internal documentation; Unit testing; Computer science; Software documentation; Redundancy (engineering); Artifact (error); Source code; Software engineering; Software; Database; Programming language; Operating system; Software development; Artificial intelligence; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000347818,0.0005015128,0.0004132499,0.0005593693,0.0002020947,0.0008103783,0.0009775696,0.0003543637,0.00002861278],"category_scores_gemma":[0.0004791607,0.0006169257,0.000314696,0.0005859755,0.00001600065,0.0005061524,0.00003394332,0.001140031,0.00001583707],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003220131,"about_ca_system_score_gemma":0.0002892949,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002921132,"about_ca_topic_score_gemma":0.000008441354,"domain_scores_codex":[0.9973547,0.0000406643,0.0004623632,0.0009604147,0.0005703781,0.0006114948],"domain_scores_gemma":[0.9965343,0.001639376,0.00009032484,0.001153114,0.0003774576,0.0002053772],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000003207008,0.00005449964,0.00005357048,0.000397171,0.0001111537,0.00001665113,0.0003224715,0.9846123,0.001098578,0.00002800569,0.00008138844,0.013221],"study_design_scores_gemma":[0.0006761223,0.0001249685,0.0005529234,0.0007654697,0.0000578888,0.0000290224,0.00002685608,0.9486277,0.04746315,0.00003865812,0.0005079666,0.001129245],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0357997,0.0002482439,0.9561712,0.00004578489,0.004971911,0.0006740469,0.00004826617,0.002039365,0.000001471003],"genre_scores_gemma":[0.3618281,0.00004338898,0.6366166,0.0000520575,0.0002474276,0.0008964552,0.0000389303,0.0001115041,0.0001654302],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.3260285,"threshold_uncertainty_score":0.9996282,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03303154572484931,"score_gpt":0.2979121345417802,"score_spread":0.2648805888169309,"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."}}