{"id":"W2169950128","doi":"10.1109/wcre.2000.891477","title":"ACCD: an algorithm for comprehension-driven clustering","year":2002,"lang":"en","type":"article","venue":"","topic":"Software Engineering Research","field":"Computer Science","cited_by":252,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; University of Waterloo","funders":"","keywords":"Program comprehension; Computer science; Cluster analysis; Cohesion (chemistry); Comprehension; Software; Data mining; Software maintenance; Software engineering; Algorithm; Theoretical computer science; Software system; Programming language; Artificial intelligence","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":[0.00009960611,0.00008966923,0.00009696842,0.000094783,0.00008384961,0.000141695,0.0007601291,0.00003989927,0.00007598393],"category_scores_gemma":[0.00005709616,0.00008296836,0.00003859359,0.0001945208,0.00001394117,0.0004295869,0.0002649546,0.000080475,0.00009449621],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003025155,"about_ca_system_score_gemma":0.00000699143,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001097382,"about_ca_topic_score_gemma":0.000003172026,"domain_scores_codex":[0.9990633,0.00001537298,0.0001079175,0.0002945119,0.0002121415,0.0003068005],"domain_scores_gemma":[0.9989112,0.0003627813,0.00001311908,0.0005075401,0.00007410513,0.0001312505],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[7.499121e-7,0.00007319995,0.0002805408,0.00001605759,0.00001251014,0.00001262965,0.0003505928,0.003367996,0.0006176123,0.0009469134,0.004837239,0.989484],"study_design_scores_gemma":[0.0002205843,0.0000883078,0.00103057,0.00000569289,7.286575e-7,0.00001366999,0.000005478879,0.9926888,0.0003452613,0.00008002843,0.005405247,0.0001156377],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002987224,0.00003580767,0.9954578,0.0002249617,0.0002760036,0.000182794,0.000001376904,0.0006068163,0.0002272021],"genre_scores_gemma":[0.1011884,0.000005428687,0.8976948,0.0001388369,0.0001049012,0.0000333002,0.000001524782,0.00001486357,0.0008180112],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9893683,"threshold_uncertainty_score":0.3383352,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05190595391670877,"score_gpt":0.2896389560241501,"score_spread":0.2377330021074413,"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."}}