{"id":"W4384026542","doi":"10.1109/icse-companion58688.2023.00077","title":"Towards Utilizing Natural Language Processing Techniques to Assist in Software Engineering Tasks","year":2023,"lang":"en","type":"article","venue":"","topic":"Software Engineering Research","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Executable; Source code; Code review; Natural language; Natural language processing; Artificial intelligence; Code (set theory); Codebase; Programming language; Task (project management); Embedding; Software; Software development; Static program analysis; Engineering","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":[0.005455108,0.002497789,0.0008525874,0.004199569,0.0006081391,0.002876875,0.002154861,0.001686972,0.002812063],"category_scores_gemma":[0.02924806,0.0007987409,0.00160479,0.00266843,0.001966456,0.009527749,0.003005833,0.004242332,0.002633972],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009653176,"about_ca_system_score_gemma":0.002555262,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002028528,"about_ca_topic_score_gemma":0.003757059,"domain_scores_codex":[0.9940268,0.002979844,0.000392176,0.001375617,0.001092261,0.0001332641],"domain_scores_gemma":[0.9753792,0.016339,0.002056814,0.003375108,0.002559644,0.0002903458],"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":[0.0001797007,0.000632268,0.004053514,0.001457113,0.0001429102,0.0003173713,0.001522938,0.04792577,0.03051429,0.03547582,0.01274456,0.8650338],"study_design_scores_gemma":[0.00006693137,0.0002037564,0.001556621,0.000318285,0.0000944897,0.0003469112,0.0008501729,0.7364427,0.02340074,0.2075373,0.02910097,0.00008108087],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005788056,0.0003462394,0.9879051,0.001104973,0.00004529424,0.0001559453,0.0002889416,0.003357855,0.001007656],"genre_scores_gemma":[0.03621997,0.0004345853,0.9607073,0.0003180304,0.00005359076,0.0001971991,0.001026003,0.0002355369,0.000807713],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005455108,"threshold_uncertainty_score":0.02884972,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01798308103686853,"score_gpt":0.3021009793877318,"score_spread":0.2841178983508633,"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."}}