{"id":"W2122525845","doi":"10.1109/icsm.2001.972708","title":"Supporting software maintenance by mining software update records","year":2002,"lang":"en","type":"article","venue":"","topic":"Software Engineering Research","field":"Computer Science","cited_by":54,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Software maintenance; Relevance (law); Context (archaeology); Software; Relation (database); Software engineering; Code (set theory); Software bug; Software system; Data mining; Programming language; Set (abstract data type)","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":[],"consensus_categories":[],"category_scores_codex":[0.0005129295,0.0002440163,0.0002339858,0.0001566263,0.0001730249,0.0002875611,0.001555255,0.0001002759,0.0008210872],"category_scores_gemma":[0.002982328,0.0002313718,0.00008870268,0.0007294968,0.00004871025,0.0007389642,0.0005398928,0.0003017828,0.000684833],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001076138,"about_ca_system_score_gemma":0.00003272458,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003061435,"about_ca_topic_score_gemma":0.000005395468,"domain_scores_codex":[0.9972482,0.00004414804,0.0003846905,0.0006954061,0.0005702735,0.001057322],"domain_scores_gemma":[0.9977769,0.0007866328,0.00009527466,0.0009270586,0.0001485819,0.0002655436],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000002283007,0.00006819048,0.07023122,0.00005074678,0.00002747759,0.0001311847,0.0005519717,0.0001640672,0.0001096943,0.0004510567,0.6572324,0.2709797],"study_design_scores_gemma":[0.003202161,0.0007204147,0.02338033,0.0005105927,0.00002730289,0.0007111324,0.0003401417,0.3385228,0.01068419,0.002497094,0.6146925,0.004711305],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01899815,0.0003473491,0.9764595,0.0008944918,0.0004620636,0.0001463833,0.000007521133,0.002199064,0.0004854958],"genre_scores_gemma":[0.2355862,0.0000561334,0.7444196,0.0009528723,0.00008663134,0.00004840405,0.000009692561,0.00006058448,0.01877992],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.3383588,"threshold_uncertainty_score":0.943507,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01790051301451302,"score_gpt":0.2614285986194306,"score_spread":0.2435280856049176,"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."}}