{'fraction_non_alphanumeric': 0.012244897959183673, 'fraction_numerical': 0.0, 'mean_word_length': 5.627027027027027, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Failure Criticality Importance Analysis in the Steady-State Condition

We propose to use failure criticality importance in the steady state as a reliability importance measure. While the existing failure criticality importance is an easily understandable and useful measure, its evaluation methods are problematic because they use simulations or Markov reward analysis of finite interval; the simulation method may show unstable results, while the Markov reward analysis is prone to an exponential increase in the number of states even for simple combinatorial models. Why do researchers persist in using a failure criticality importance for finite intervals, while analyses in finite intervals are known to be more difficult than steady-state analyses? Because research on failure criticality importance starts with simulations and simulations must stop within a finite interval for technical reasons, we emphasize that the analytical method would be easier in a steady state, which is commonly assumed in real system. Here, we give a method to evaluate the failure criticality importance in the steady-state condition with high efficiency for the combinatorial model and demonstrate its effectiveness in numerical examples.