{"id":"W2535082093","doi":"10.1109/wcre.2005.30","title":"Source versus Object Code Extraction for Recovering Software Architecture","year":2006,"lang":"en","type":"article","venue":"","topic":"Software Engineering Research","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Source code; KPI-driven code analysis; Extractor; Object code; Code (set theory); Object (grammar); Architecture; Programming language; Software architecture; Software; Software engineering; Code generation; Software development; Operating system; Software quality; Artificial intelligence; Key (lock); Engineering","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":[0.000205358,0.0001213911,0.0001010179,0.0001329524,0.0001103008,0.0001585364,0.0004775463,0.00006757348,0.0000155628],"category_scores_gemma":[0.000578645,0.0001167629,0.00007620502,0.0002703661,0.00001431444,0.0002391495,0.0001103386,0.0001668269,0.00002973074],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001020992,"about_ca_system_score_gemma":0.00004809606,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001005861,"about_ca_topic_score_gemma":0.00008671563,"domain_scores_codex":[0.9988816,0.00001601629,0.0001317998,0.0003463129,0.0002591579,0.0003651252],"domain_scores_gemma":[0.9978625,0.001580681,0.00002898259,0.0004062658,0.00006395901,0.00005757188],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001972051,0.0001135488,0.002984792,0.0001475064,0.00005588043,0.00001481687,0.0002575766,0.3611656,0.006850339,0.003994034,0.0127204,0.6114984],"study_design_scores_gemma":[0.007521718,0.001353513,0.04610999,0.000140739,0.00003364042,0.0001947778,0.00006150252,0.2642319,0.07693896,0.01447045,0.5865791,0.002363658],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01847057,0.00005805806,0.9789826,0.0002155187,0.0006486414,0.0002073196,0.000003110764,0.00107375,0.000340418],"genre_scores_gemma":[0.2974148,0.000002887288,0.6979825,0.00004320469,0.0004231279,0.00009437103,0.00001120473,0.00004064515,0.003987252],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.6091347,"threshold_uncertainty_score":0.4761453,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01881414732745051,"score_gpt":0.275957905899446,"score_spread":0.2571437585719955,"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."}}