{"id":"W2081943844","doi":"10.1016/j.cag.2005.03.007","title":"3D visualization techniques to support slicing-based program comprehension","year":2005,"lang":"en","type":"article","venue":"Computers & Graphics","topic":"Software Engineering Research","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada; Concordia University","keywords":"Computer science; Program slicing; Visualization; Program comprehension; Software visualization; Slicing; Source code; Rendering (computer graphics); Software; Comprehension; Human–computer interaction; Software system; Programming language; Data mining; Component-based software engineering; Artificial intelligence; Computer graphics (images)","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0007490834,0.001580922,0.000632509,0.00121364,0.0004480911,0.001427631,0.001305397,0.0008632455,0.01680347],"category_scores_gemma":[0.005105422,0.000773124,0.000864089,0.0009703882,0.0004266017,0.002237661,0.001718888,0.001878254,0.00147414],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003167433,"about_ca_system_score_gemma":0.0006422802,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001459534,"about_ca_topic_score_gemma":0.002907882,"domain_scores_codex":[0.9995055,0.0001315761,0.00004360249,0.00005691393,0.0002223329,0.00004003723],"domain_scores_gemma":[0.9962585,0.001888072,0.0002476419,0.000747379,0.0007259486,0.0001324124],"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":[0.0005094944,0.000297536,0.002040175,0.001010591,0.000141941,0.0006937362,0.003456967,0.03173137,0.2479424,0.0380005,0.03966569,0.6345096],"study_design_scores_gemma":[0.0002443321,0.0002865352,0.001995541,0.0002545995,0.0001850074,0.0007317572,0.0003441332,0.6196967,0.2550193,0.03616505,0.08491421,0.0001628318],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01179799,0.0001915271,0.9570017,0.000258217,0.00008207641,0.00007005045,0.0002714512,0.02701859,0.003308388],"genre_scores_gemma":[0.1577762,0.0005232284,0.8310913,0.0002264161,0.00008363077,0.0001978392,0.0007704651,0.005988224,0.003342697],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01680347,"threshold_uncertainty_score":0.0562132,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0232504246986767,"score_gpt":0.3203594905580445,"score_spread":0.2971090658593677,"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."}}