{"id":"W7153116829","doi":"10.63282/3050-9262.ijaidsml-v5i3p121","title":"AI-Augmented Software Engineering: A Holistic Approach to Reliability, Security, and Lifecycle Optimization","year":2024,"lang":"","type":"article","venue":"International Journal of Artificial Intelligence Data Science and Machine Learning","topic":"Software Reliability and Analysis Research","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Software quality; Software development; Quality (philosophy); Vulnerability (computing); Implementation; Model-driven architecture; Software system; Software; System lifecycle","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":[0.002030425,0.001561522,0.0008138845,0.002729824,0.001102711,0.006522796,0.003010202,0.002375831,0.002778751],"category_scores_gemma":[0.002843986,0.0007377013,0.001222209,0.002267826,0.008050906,0.006096698,0.004289697,0.004635837,0.0005800839],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002680086,"about_ca_system_score_gemma":0.003876733,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003185782,"about_ca_topic_score_gemma":0.002857049,"domain_scores_codex":[0.9979114,0.0008860109,0.0001165337,0.000325841,0.0006302802,0.0001298719],"domain_scores_gemma":[0.9977146,0.001308702,0.0001839511,0.0003625327,0.0002550615,0.0001750234],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00002961175,0.00009306166,0.0008104389,0.0005183649,0.000100276,0.0001931879,0.0007334736,0.07722025,0.002188531,0.8341987,0.002966393,0.08094771],"study_design_scores_gemma":[0.00001293668,0.0000806612,0.0004589623,0.0003245467,0.0000508299,0.0001329376,0.0003788437,0.1480495,0.001070287,0.8024995,0.04689464,0.00004640225],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.005005201,0.0107849,0.9468923,0.006426266,0.0002688666,0.00009001911,0.00008622792,0.0004824632,0.02996379],"genre_scores_gemma":[0.2760357,0.01802997,0.6935602,0.001207454,0.000954317,0.0003563856,0.0002175668,0.0002201568,0.009418286],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006522796,"threshold_uncertainty_score":0.01944542,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0680045401483019,"score_gpt":0.3636618901234947,"score_spread":0.2956573499751928,"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."}}