{"id":"W2616384889","doi":"10.1109/icst.2017.24","title":"IPA: Error Propagation Analysis of Multi-Threaded Programs Using Likely Invariants","year":2017,"lang":"en","type":"preprint","venue":"","topic":"Radiation Effects in Electronics","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Horizon 2020 Framework Programme","keywords":"Fault injection; Computer science; Robustness (evolution); Fault (geology); Invariant (physics); Fault coverage; Fault tolerance; Parallel computing; Algorithm; Distributed computing; Programming language; Mathematics; Engineering; Software; Electrical engineering; Electronic circuit","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004007445,0.0003352474,0.0007053196,0.0005766346,0.00007356425,0.0001234688,0.0004865653,0.000455929,0.00003473269],"category_scores_gemma":[0.0001195529,0.0003391512,0.0002839543,0.0003911557,0.00005897734,0.0001643772,0.0001495689,0.000518024,0.00000707539],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003734656,"about_ca_system_score_gemma":0.0001572301,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003791863,"about_ca_topic_score_gemma":0.0005747833,"domain_scores_codex":[0.9983628,0.00006524324,0.0005229697,0.0003906486,0.0003094424,0.0003488972],"domain_scores_gemma":[0.9983104,0.00003738302,0.0003918587,0.001029124,0.0001587122,0.00007249732],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000004361219,0.00006466352,0.001138773,0.0003245083,0.002341572,0.000001857067,0.000241949,0.984436,0.00144608,0.0001178481,0.00001935481,0.009863081],"study_design_scores_gemma":[0.0002247175,0.00002036759,0.004307567,0.0000950177,0.001459389,8.303368e-7,0.000009691269,0.9894344,0.004001983,0.0000823934,0.00003539994,0.0003282497],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6223995,0.0006364195,0.3721468,0.00002920509,0.0009742608,0.001796871,0.00003897231,0.0007032049,0.001274834],"genre_scores_gemma":[0.9764548,0.00003218515,0.0229985,0.000005588751,0.00006674348,0.00008244788,0.0002065208,0.00006857347,0.00008463921],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3540553,"threshold_uncertainty_score":0.9999061,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06897228285210766,"score_gpt":0.3160404568122323,"score_spread":0.2470681739601246,"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."}}