{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004975728,0.001438196,0.001055354,0.004216187,0.000502286,0.002028792,0.002013198,0.001448495,0.005908828],"category_scores_gemma":[0.05983199,0.0009348184,0.001275299,0.002070534,0.0007737704,0.001818247,0.002032054,0.001155348,0.002950515],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007804861,"about_ca_system_score_gemma":0.001766186,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009245547,"about_ca_topic_score_gemma":0.001040761,"domain_scores_codex":[0.9915909,0.002821476,0.000867408,0.001129504,0.003213935,0.0003768012],"domain_scores_gemma":[0.9266635,0.04100972,0.005104288,0.01516866,0.01121557,0.0008383038],"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":[0.0007447159,0.0006466645,0.02207063,0.001229063,0.0001879005,0.001748579,0.001286031,0.04636938,0.05480761,0.02311805,0.02578533,0.8220062],"study_design_scores_gemma":[0.0004351157,0.001059978,0.007760834,0.0005326889,0.0002315466,0.002796303,0.000412676,0.5466937,0.3340314,0.03947902,0.06634304,0.0002236855],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.096995,0.0003344115,0.8557764,0.000284873,0.0002057797,0.0007329653,0.001840688,0.03698016,0.006849734],"genre_scores_gemma":[0.3003529,0.0001944478,0.6826017,0.0002303398,0.00007078141,0.0007251151,0.005612465,0.006055742,0.004156501],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005908828,"threshold_uncertainty_score":0.02631444,"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."}}