[go: up one dir, main page]

Rudenko et al., 2018 - Google Patents

The secondary software faults number evaluation based on correction of the experimental data exponential line approximation

Rudenko et al., 2018

Document ID
7225944681620743780
Author
Rudenko O
Odarushchenko E
Rudenko Z
Rudenko M
Publication year
Publication venue
2018 IEEE 9th International Conference on Dependable Systems, Services and Technologies (DESSERT)

External Links

Snippet

The paper presents an analysis of existing approaches to the secondary software faults problem. The new method for secondary software faults assessment is considered. It is based on the comparison of secondary software faults statistics and the data from corrected …
Continue reading at ieeexplore.ieee.org (other versions)

Classifications

    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F11/00Error detection; Error correction; Monitoring
    • G06F11/36Preventing errors by testing or debugging software
    • G06F11/3668Software testing
    • G06F11/3672Test management
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F11/00Error detection; Error correction; Monitoring
    • G06F11/30Monitoring
    • G06F11/34Recording or statistical evaluation of computer activity, e.g. of down time, of input/output operation; Recording or statistical evaluation of user activity, e.g. usability assessment
    • G06F11/3409Recording or statistical evaluation of computer activity, e.g. of down time, of input/output operation; Recording or statistical evaluation of user activity, e.g. usability assessment for performance assessment
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/50Computer-aided design
    • G06F17/5009Computer-aided design using simulation
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F11/00Error detection; Error correction; Monitoring
    • G06F11/07Error detection; Error correction; Monitoring responding to the occurence of a fault, e.g. fault tolerance
    • G06F11/0703Error or fault processing not based on redundancy, i.e. by taking additional measures to deal with the error or fault not making use of redundancy in operation, in hardware, or in data representation
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F11/00Error detection; Error correction; Monitoring
    • G06F11/22Detection or location of defective computer hardware by testing during standby operation or during idle time, e.g. start-up testing
    • GPHYSICS
    • G05CONTROLLING; REGULATING
    • G05BCONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B23/00Testing or monitoring of control systems or parts thereof
    • G05B23/02Electric testing or monitoring
    • G05B23/0205Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
    • G05B23/0218Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterised by the fault detection method dealing with either existing or incipient faults
    • G05B23/0243Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterised by the fault detection method dealing with either existing or incipient faults model based detection method, e.g. first-principles knowledge model
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10Complex mathematical operations
    • GPHYSICS
    • G05CONTROLLING; REGULATING
    • G05BCONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B23/00Testing or monitoring of control systems or parts thereof
    • G05B23/02Electric testing or monitoring
    • G05B23/0205Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
    • G05B23/0218Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterised by the fault detection method dealing with either existing or incipient faults
    • G05B23/0224Process history based detection method, e.g. whereby history implies the availability of large amounts of data
    • G05B23/024Quantitative history assessment, e.g. mathematical relationships between available data; Functions therefor; Principal component analysis [PCA]; Partial least square [PLS]; Statistical classifiers, e.g. Bayesian networks, linear regression or correlation analysis; Neural networks
    • GPHYSICS
    • G05CONTROLLING; REGULATING
    • G05BCONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B23/00Testing or monitoring of control systems or parts thereof
    • G05B23/02Electric testing or monitoring
    • G05B23/0205Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
    • G05B23/0218Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterised by the fault detection method dealing with either existing or incipient faults
    • G05B23/0224Process history based detection method, e.g. whereby history implies the availability of large amounts of data
    • G05B23/0227Qualitative history assessment, whereby the type of data acted upon, e.g. waveforms, images or patterns, is not relevant, e.g. rule based assessment; if-then decisions
    • G05B23/0229Qualitative history assessment, whereby the type of data acted upon, e.g. waveforms, images or patterns, is not relevant, e.g. rule based assessment; if-then decisions knowledge based, e.g. expert systems; genetic algorithms
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F2201/00Indexing scheme relating to error detection, to error correction, and to monitoring
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00Subject matter not provided for in other groups of this subclass
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00Computer systems utilising knowledge based models

Similar Documents

Publication Publication Date Title
US9063856B2 (en) Method and system for detecting symptoms and determining an optimal remedy pattern for a faulty device
US9459947B2 (en) Error report processing using call stack similarity
Zhang et al. Fault detection and diagnosis based on particle filters combined with interactive multiple-model estimation in dynamic process systems
Zhang Time series: Autoregressive models ar, ma, arma, arima
CN113900845A (en) Method and storage medium for micro-service fault diagnosis based on neural network
Rudenko et al. The secondary software faults number evaluation based on correction of the experimental data exponential line approximation
CN110570544A (en) Aircraft fuel system fault identification method, device, equipment and storage medium
Perez et al. Leveraging Qualitative Reasoning to Improve SFL.
CN109271319A (en) A kind of prediction technique of the software fault based on panel Data Analyses
Asraful Haque et al. A logistic growth model for software reliability estimation considering uncertain factors
Aggarwal et al. Causal modeling based fault localization in cloud systems using golden signals
Algarni et al. Applying software design metrics to developer story: a supervised machine learning analysis
US20170317890A1 (en) Inferring Component Parameters for Components in a Network
Aremu et al. Kullback-leibler divergence constructed health indicator for data-driven predictive maintenance of multi-sensor systems
CN117520040A (en) Micro-service fault root cause determining method, electronic equipment and storage medium
Reeb et al. Validation of composite systems by discrepancy propagation
Aranha et al. Estimating manual test execution effort and capacity based on execution points
D'Antona et al. Bad data detection and identification in power system state estimation with network parameters uncertainty
Zhang et al. A comparison of different statistics for detecting multiplicative faults in multivariate statistics-based fault detection approaches
Gonzalez et al. Control loop diagnosis with ambiguous historical operating modes: Part 1. A proportional parametrization approach
Haque et al. An NHPP-based SRGM with time dependent growth process
Mosin A technique of analog circuits testing and diagnosis based on neuromorphic classifier
Saxena et al. Realiability Assessment Model to Estimate Quality of the Effective E-Procurement Process in Adoption
Kukadiya et al. Synchrophasor Measurement Prediction Using Modified Time-Series Mixer Model Under Cyber Attacks
Ramirez et al. Fault detection and monitoring using an information-driven strategy: Method, theory, and application