{"id":"W4412704074","doi":"10.1145/3696630.3728518","title":"From Overload to Insight: Bridging Code Search and Code Review with LLMs","year":2025,"lang":"en","type":"article","venue":"","topic":"Software Engineering Research","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Bridging (networking); Computer science; Information overload; Code (set theory); Programming language; Computer security; World Wide Web","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":[0.09260549,0.001739687,0.001856944,0.01103403,0.00604061,0.01567342,0.005326723,0.005565762,0.008125996],"category_scores_gemma":[0.3200614,0.001821451,0.001083844,0.003845865,0.008641199,0.02328248,0.02798433,0.004533983,0.004799517],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003737321,"about_ca_system_score_gemma":0.01244582,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001353346,"about_ca_topic_score_gemma":0.002125183,"domain_scores_codex":[0.7906985,0.1718117,0.007150197,0.009504745,0.01791739,0.00291744],"domain_scores_gemma":[0.5143834,0.3737913,0.0341418,0.03743374,0.02875559,0.01149424],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001841514,0.0005045772,0.01033584,0.003760627,0.0002218369,0.001995373,0.1643019,0.00277921,0.02215681,0.08829262,0.03519425,0.6686154],"study_design_scores_gemma":[0.000526114,0.001077885,0.005911162,0.003167818,0.0004257218,0.002390585,0.04555167,0.05906064,0.02014523,0.3683117,0.4927492,0.0006823674],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06481886,0.005953672,0.8290626,0.05294329,0.001937246,0.001649117,0.0002805482,0.01170892,0.03164578],"genre_scores_gemma":[0.5540481,0.00198777,0.4147849,0.009862228,0.002689858,0.002126211,0.0003886627,0.002180025,0.01193232],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.09260549,"threshold_uncertainty_score":0.4897505,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0195272804877584,"score_gpt":0.2974078624599623,"score_spread":0.2778805819722039,"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."}}