{"id":"W147643174","doi":"","title":"Predicting Maintainability expressed as Change Impact: A Machine-learning-based Approach.","year":2009,"lang":"en","type":"article","venue":"Software Engineering and Knowledge Engineering","topic":"Complex Systems and Decision Making","field":"Decision Sciences","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Maintainability; Computer science; Machine learning; Change impact analysis; Artificial intelligence; Reliability engineering; Engineering; 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":["metaresearch","metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.002367537,0.0005554742,0.0007300924,0.0008867881,0.000254495,0.0004789873,0.000597075,0.0002044418,0.00005912397],"category_scores_gemma":[0.01078464,0.0004617866,0.0003172253,0.001308876,0.00002830664,0.0004094461,0.0001936702,0.0006546491,0.00004308333],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001675132,"about_ca_system_score_gemma":0.00008111169,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007446027,"about_ca_topic_score_gemma":0.000003933953,"domain_scores_codex":[0.9964995,0.0001060026,0.0008578418,0.0009437487,0.0008210705,0.0007717834],"domain_scores_gemma":[0.99634,0.001964123,0.0001701438,0.0007564249,0.000288314,0.0004810272],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00008957807,0.0002473018,0.04701801,0.0002593437,0.00006819065,0.00005995238,0.003994822,0.8942684,0.00119226,0.002250297,0.000331812,0.05022006],"study_design_scores_gemma":[0.0005836952,0.0002119391,0.0250239,0.0001781853,0.00001503643,0.00005247957,0.000112984,0.9606966,0.00009793896,0.0003690554,0.01211553,0.0005426327],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3388665,0.005501664,0.6523703,0.00004721714,0.0006668473,0.0005832452,0.00002421058,0.001555491,0.0003844872],"genre_scores_gemma":[0.9865475,0.000006193965,0.01264641,0.00002655176,0.000454081,0.00006397392,0.000007313949,0.0000619047,0.0001861034],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6476809,"threshold_uncertainty_score":0.9997834,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04966882006592686,"score_gpt":0.323083148430964,"score_spread":0.2734143283650371,"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."}}