{"id":"W2002641269","doi":"10.1007/s10664-014-9315-y","title":"An empirical study on the importance of source code entities for requirements traceability","year":2014,"lang":"en","type":"article","venue":"Empirical Software Engineering","topic":"Software Engineering Research","field":"Computer Science","cited_by":39,"is_retracted":false,"has_abstract":false,"ca_institutions":"Polytechnique Montréal; University of Waterloo","funders":"","keywords":"Computer science; Information retrieval; Traceability; Source code; Weighting; Search engine indexing; Rank (graph theory); Data mining; Software engineering; Programming language","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":[0.001873086,0.0002842368,0.0003668174,0.0001364893,0.0001418093,0.0001188787,0.001508769,0.00009747656,0.00001337047],"category_scores_gemma":[0.007115993,0.0002192052,0.0001447929,0.000529683,0.00005986869,0.000294295,0.0002212136,0.0003525707,0.000008430259],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001298583,"about_ca_system_score_gemma":0.0000515565,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005666698,"about_ca_topic_score_gemma":0.000003908701,"domain_scores_codex":[0.9973527,0.0001433274,0.0004968063,0.0006553447,0.0007836292,0.0005681532],"domain_scores_gemma":[0.9929591,0.005191865,0.00008613623,0.001404178,0.0001569606,0.0002017311],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00001615013,0.0004525878,0.9780486,0.00006369089,0.00004243866,0.000001967894,0.001358424,0.01806793,0.00008835326,0.0001730252,0.0008524997,0.0008343082],"study_design_scores_gemma":[0.0006402925,0.001369689,0.8526644,0.00004326224,0.00001411217,0.000003043899,0.00009201022,0.1393325,0.0009954225,0.000349986,0.004088982,0.0004062811],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5061007,0.00001622538,0.4925052,0.0002494302,0.0001613824,0.0004102245,0.000004461547,0.0005493321,0.00000311823],"genre_scores_gemma":[0.9789586,4.94707e-7,0.0204433,0.0001796233,0.000136644,0.0001958962,0.000003125708,0.00004529169,0.0000370387],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4728579,"threshold_uncertainty_score":0.8938928,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05999686482834988,"score_gpt":0.3489345172867725,"score_spread":0.2889376524584226,"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."}}