{"id":"W2106095006","doi":"10.1142/s021819401000492x","title":"DYNAMIC KNOWLEDGE EXTRACTION FROM SOFTWARE SYSTEMS USING SEQUENTIAL PATTERN MINING","year":2010,"lang":"en","type":"article","venue":"International Journal of Software Engineering and Knowledge Engineering","topic":"Software Engineering Research","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Computer science; Software system; Software construction; Source code; Software sizing; Software; Cohesion (chemistry); Software visualization; Software framework; Software development; Software engineering; Static program analysis; Software metric; Component-based software engineering; Data mining; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001150509,0.001196702,0.0009731932,0.009567321,0.0006655777,0.001355924,0.001324691,0.0007259418,0.001035189],"category_scores_gemma":[0.00687296,0.0005159712,0.001445233,0.006309137,0.0006013985,0.001972535,0.001173063,0.0007231902,0.0007954524],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007416804,"about_ca_system_score_gemma":0.001979457,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004059378,"about_ca_topic_score_gemma":0.005768519,"domain_scores_codex":[0.998007,0.0003377659,0.0002742155,0.0004457254,0.0008183917,0.0001168664],"domain_scores_gemma":[0.9954045,0.002342114,0.000595885,0.000719123,0.00083564,0.0001027005],"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":[0.0002109105,0.0003350909,0.01783111,0.001367452,0.0002646508,0.001718029,0.0008322015,0.04899409,0.02769135,0.007278495,0.003905064,0.8895715],"study_design_scores_gemma":[0.00009501658,0.0004159029,0.01435977,0.0003001567,0.0003608637,0.002541282,0.001144977,0.8308871,0.05644755,0.06993409,0.02339474,0.0001185634],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.07945362,0.0007260108,0.9109488,0.00039151,0.00003772651,0.0005184684,0.002322803,0.003486755,0.002114333],"genre_scores_gemma":[0.2621838,0.0007872328,0.7285532,0.00007213835,0.00002676125,0.0004182422,0.006782729,0.0001558148,0.001020058],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.009567321,"threshold_uncertainty_score":0.008071482,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01469036713613227,"score_gpt":0.2840261469821888,"score_spread":0.2693357798460566,"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."}}