{"id":"W2041974913","doi":"10.1109/scam.2010.25","title":"Deriving Coupling Metrics from Call Graphs","year":2010,"lang":"en","type":"preprint","venue":"","topic":"Software Engineering Research","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"","keywords":"Computer science; Coupling (piping); Call graph; Theoretical computer science; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003155248,0.001538199,0.000782668,0.008450979,0.0006284838,0.001978602,0.001285741,0.0009435972,0.00169653],"category_scores_gemma":[0.05423424,0.0005250866,0.000900345,0.006236,0.0008310035,0.003353243,0.001594504,0.001066403,0.0005057254],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001343946,"about_ca_system_score_gemma":0.001481686,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003802138,"about_ca_topic_score_gemma":0.003978667,"domain_scores_codex":[0.9938193,0.001775221,0.0005473966,0.0006656598,0.00289389,0.0002986154],"domain_scores_gemma":[0.9605137,0.02260513,0.003341839,0.006045233,0.007082403,0.0004116375],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0003198519,0.0003368542,0.05731132,0.0008490148,0.0004033839,0.0002706188,0.0009503913,0.2841047,0.02796737,0.06524193,0.005694849,0.5565497],"study_design_scores_gemma":[0.00004090074,0.0002134852,0.01940538,0.00007368808,0.0001351674,0.0002791476,0.0002369373,0.8851033,0.02660241,0.06133635,0.006476974,0.0000962812],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.2366651,0.0004193668,0.7521327,0.0001543523,0.00005579107,0.0001988339,0.0008804853,0.005269168,0.004224221],"genre_scores_gemma":[0.6537903,0.0002972564,0.3405191,0.00004952445,0.0000310334,0.0003035448,0.002548523,0.001551137,0.0009096731],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.008450979,"threshold_uncertainty_score":0.01668674,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02888892690797957,"score_gpt":0.2791611540567684,"score_spread":0.2502722271487888,"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."}}