{"id":"W1985346994","doi":"10.5555/2819009.2819022","title":"Comparing software architecture recovery techniques using accurate dependencies","year":2015,"lang":"en","type":"article","venue":"","topic":"Software System Performance and Reliability","field":"Computer Science","cited_by":51,"is_retracted":false,"has_abstract":true,"ca_institutions":"Google (Canada); University of Waterloo","funders":"","keywords":"Computer science; Architecture; Software; Software architecture; Implementation; Ground truth; Code (set theory); Quality (philosophy); Data mining; Artificial intelligence; Software engineering; Programming language","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0006107987,0.0001667541,0.0002330061,0.0001194964,0.0001276797,0.0002027688,0.0007735118,0.0001004517,0.000004508342],"category_scores_gemma":[0.0001518305,0.000122428,0.00007461228,0.0003471984,0.00004503891,0.0008542458,0.0004308796,0.0001809836,0.00005225164],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000125381,"about_ca_system_score_gemma":0.0002058856,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002135922,"about_ca_topic_score_gemma":0.00004696547,"domain_scores_codex":[0.9986469,0.00007146625,0.0002724805,0.0003616282,0.0003401639,0.0003074097],"domain_scores_gemma":[0.9988582,0.0000766303,0.00009359299,0.0006535927,0.000176603,0.0001414387],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001798765,0.000454143,0.4794604,0.0007564697,0.0002339183,0.0001523224,0.01056799,0.1006622,0.003248231,0.003539772,0.01687707,0.3838677],"study_design_scores_gemma":[0.004814124,0.002463453,0.0385069,0.002089146,0.0001480932,0.003952602,0.002732506,0.4143883,0.1913022,0.2838465,0.0469887,0.00876751],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1569743,0.0001066725,0.8397117,0.00009046181,0.0004367188,0.0001656032,6.214471e-7,0.001029036,0.001484887],"genre_scores_gemma":[0.6059119,0.000004217903,0.3936401,0.0001677788,0.00009189604,0.000009359062,0.000001438975,0.000008465505,0.0001648894],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.4489375,"threshold_uncertainty_score":0.4992468,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06630326630733942,"score_gpt":0.2837157105576726,"score_spread":0.2174124442503332,"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."}}