{"id":"W2587703861","doi":"10.1002/smr.1842","title":"The relationship between evolutionary coupling and defects in large industrial software","year":2017,"lang":"en","type":"article","venue":"Journal of Software Evolution and Process","topic":"Software Engineering Research","field":"Computer Science","cited_by":34,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"Engineering and Physical Sciences Research Council; Natural Sciences and Engineering Research Council of Canada; Türkiye Bilimler Akademisi; Türkiye Bilimsel ve Teknolojik Araştırma Kurumu","keywords":"Software; Software evolution; Software system; Computer science; Correlation; Coupling (piping); Process (computing); Data mining; Software engineering; Software construction; Engineering; Mathematics; Programming language","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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.001718058,0.0001254881,0.0001964818,0.0002160776,0.001074308,0.0003971251,0.0007359896,0.0001491735,9.919111e-7],"category_scores_gemma":[0.01795382,0.00009745141,0.00004241246,0.0002296262,0.000130618,0.001206203,0.0002471955,0.0007116953,0.000001875007],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000125708,"about_ca_system_score_gemma":0.0003251216,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001152606,"about_ca_topic_score_gemma":0.00001191995,"domain_scores_codex":[0.9985821,0.00005152612,0.0003812259,0.0001964793,0.00047615,0.0003125464],"domain_scores_gemma":[0.9956683,0.003210683,0.0003665556,0.000303061,0.0002910246,0.0001603675],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00001858566,0.00001315279,0.9956474,0.00003540828,0.000009486977,0.00001086749,0.0001650278,0.0001554351,7.164695e-7,0.0008344409,0.000176459,0.002933009],"study_design_scores_gemma":[0.0008942885,0.00007766183,0.9875922,0.0002105106,0.000007630864,0.00006570666,0.00004031736,0.00106204,0.00000484674,0.009593176,0.0003399044,0.0001117527],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7702552,0.003114856,0.2254871,0.0005447994,0.0004165435,0.0001226763,0.000004241218,0.00005113014,0.000003406963],"genre_scores_gemma":[0.9960272,0.00006128009,0.003544658,0.000009341553,0.0003092523,0.000005443088,7.650076e-7,0.00001005424,0.00003201104],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.225772,"threshold_uncertainty_score":0.9903184,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05343133578773611,"score_gpt":0.3163491672404003,"score_spread":0.2629178314526642,"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."}}