{"id":"W2127176799","doi":"10.1109/icac.2005.49","title":"Quickly Finding Known Software Problems via Automated Symptom Matching","year":2005,"lang":"en","type":"article","venue":"","topic":"Software Engineering Research","field":"Computer Science","cited_by":81,"is_retracted":false,"has_abstract":true,"ca_institutions":"IBM (Canada)","funders":"","keywords":"Computer science; Matching (statistics); Software; Component (thermodynamics); Pattern matching; Software system; Product (mathematics); Software product line; Data mining; Artificial intelligence; Programming language; Software development; Mathematics","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":[0.002051441,0.001137225,0.00149612,0.004485492,0.000877062,0.001871364,0.003307816,0.001875362,0.004402019],"category_scores_gemma":[0.01353286,0.0006507625,0.0008972441,0.002524125,0.0006983352,0.003773801,0.002665247,0.001215603,0.001829311],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005456515,"about_ca_system_score_gemma":0.001347059,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003002002,"about_ca_topic_score_gemma":0.003530296,"domain_scores_codex":[0.9968732,0.0006258219,0.0003399739,0.0007911292,0.001204555,0.0001653212],"domain_scores_gemma":[0.9899455,0.004789161,0.001669833,0.001923798,0.001366598,0.0003051227],"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":[0.0006735199,0.0008939485,0.02266699,0.0006891405,0.0002143439,0.0009344078,0.001070968,0.01304234,0.06796072,0.00492192,0.01264363,0.8742882],"study_design_scores_gemma":[0.0003494768,0.0009141897,0.01803435,0.0001174256,0.0002742835,0.003462709,0.001141401,0.8012028,0.1252773,0.02745212,0.02156354,0.0002104285],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.104595,0.0004495477,0.8413517,0.0005543198,0.00007198685,0.0004934425,0.000913521,0.0492807,0.002289756],"genre_scores_gemma":[0.2662235,0.0001968659,0.7289425,0.0001799404,0.00003625594,0.0001907111,0.001949621,0.0006099676,0.001670556],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004485492,"threshold_uncertainty_score":0.01472622,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01508583835480862,"score_gpt":0.2669925024991173,"score_spread":0.2519066641443086,"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."}}