scholarly journals A Novel System Reliability Modeling of Hardware, Software, and Interactions of Hardware and Software

Mathematics ◽  
2019 ◽  
Vol 7 (11) ◽  
pp. 1049 ◽  
Author(s):  
Mengmeng Zhu ◽  
Hoang Pham
Keyword(s):  
System Reliability ◽  
Reliability Estimation ◽  
Reliability Prediction ◽  
Reliability Modeling ◽  
System Failures ◽  
Software Failures ◽  
Reliability Models ◽  
Software Reliability Models ◽  
Accuracy Comparison ◽  
Hardware Failures

In the past few decades, a great number of hardware and software reliability models have been proposed to address hardware failures in hardware subsystems and software failures in software subsystems, respectively. The interactions between hardware and software subsystems are often neglected in order to simplify reliability modeling, and hence, most existing reliability models assumed hardware subsystems and software subsystem are independent of each other. However, this may not be true in reality. In this study, system failures are classified into three categories, which are hardware failures, software failures, and hardware-software interaction failures. The main contribution of our research is that we further classify hardware-software interaction failures into two groups: software-induced hardware failures and hardware-induced software failures. A Markov-based unified system reliability modeling incorporating all three categories of system failures is developed in this research, which provides a novel and practical perspective to define system failures and further improve reliability prediction accuracy. Comparison of system reliability estimation between the reliability models with and without considering hardware-software interactions is elucidated in the numerical example. The impacts on system reliability prediction as the changes of transition parameters are also illustrated by the numerical examples.

scholarly journals Software Matrices Selection for a SDLC Based Software Reliability Prediction Model

2019 ◽  
Vol 8 (2S6) ◽  
pp. 959-963
Keyword(s):  
Software Reliability ◽  
Relevant Literature ◽  
Error Distribution ◽  
Reliability Estimation ◽  
Reliability Prediction ◽  
Development Phase ◽  
Reliability Models ◽  
Software Reliability Models ◽  
Fault Density ◽  
Selection For

Software reliability is one of the essential factors of quality in software engineering like other quality attributes as functionality, usability, maintainability, performance, serviceability, documentation etc. From last few years, several software reliability models have been developed. There is lack of relevant literature which focuses on processes related to SDLC. A SDLC based structure for measurement of reliability has been proposed. Identified software reliability measures which are majorly take place in all levels of early software development phase of SDLC. Considering all measures for reliability estimation will be costly and time taking. So measures are identified which are taking place at each development phase and have high synthetic weight according to selecting criteria based on expert judgment and multi criteria decision making technique. Based on the grading, top ranked measures like completeness, error distribution, fault density etc are identified. Use of recommended metrics will make software reliability estimation more effective and reliable

scholarly journals A New Method on Software Reliability Prediction

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Zhang Xiaonan ◽  
Yang Junfeng ◽  
Du Siliang ◽  
Huang Shudong
Keyword(s):  
Life Cycle ◽  
Software Reliability ◽  
Reliability Prediction ◽  
Software Life Cycle ◽  
Reliability Models ◽  
Software Reliability Models ◽  
Svm Model ◽  
Software Reliability Prediction ◽  
Simulation Results ◽  
Improved Model

As we all know, relevant data during software life cycle can be used to analyze and predict software reliability. Firstly, the major disadvantages of the current software reliability models are discussed. And then based on analyzing classic PSO-SVM model and the characteristics of software reliability prediction, some measures of the improved PSO-SVM model are proposed, and the improved model is established. Lastly, simulation results show that compared with classic models, the improved model has better prediction precision, better generalization ability, and lower dependence on the number of samples, which is more applicable for software reliability prediction.

IMPACT OF SOFTWARE FAILURES ON THE RELIABILITY OF A MAN-COMPUTER SYSTEM

2006 ◽  
Vol 13 (02) ◽  
pp. 149-156
Author(s):  
WOJCIECH ZAMOJSKI ◽  
DARIUSZ CABAN
Keyword(s):  
System Analysis ◽  
Environmental Changes ◽  
Human Interaction ◽  
Software Faults ◽  
Human Errors ◽  
Software Failures ◽  
Reliability Models ◽  
Software Reliability Models ◽  
Key Factor ◽  
The Impact

Software failures and human errors are the most common reasons of inoparability of computer systems. Computers are increasingly reliable, but the level of transcient faults, caused by errors hidden in the programs, remains the same. For this reason software is becoming the key factor in the synthesis of highly reliable systems. Software errors do not result from operation, they either exist from the start or are inserted when patching or upgrading it. Some errors result from incorrect human interaction or unexpected environmental changes. Assessment of software impact requires functional-reliability approach to reliability analysis: the software fault occurs when it causes incorrect operation and not when it is introduced to the system. It is proposed to use software reliability models in system analysis, to predict the intensity of software faults. The software recovery after a failure is realized by restarts of various extend (microrestarts, minirestarts and macrorestarts). The impact of software failures and restarts on system availability is assessed.

scholarly journals FORECASTING THE NUMBER OF FAILURES OF THE STEERING SYSTEM COMPONENTS WITH THE USE OF THE GREY SYSTEM THEORY METHOD

2021 ◽  
Vol 112 ◽  
pp. 85-97
Author(s):  
Tomasz KĄDZIOŁKA ◽  
Kazimierz OPOKA
Keyword(s):  
System Theory ◽  
System Reliability ◽  
Motor Vehicle ◽  
High Reliability ◽  
Steering System ◽  
Grey System Theory ◽  
Reliability Estimation ◽  
Grey System ◽  
System Failures ◽  
Rolling Elements

Steering systems are one of the most important components of a car and have a direct impact on safety and driving comfort. Therefore, high reliability is required of them. One of the methods of object reliability estimation may be the grey system theory. This method can be used not only to calculate the number of failures, but also to calculate the wear of mating parts, and the vibrations of engines and rolling elements. This work presents the use of the grey system theory for the examination of motor vehicle steering system reliability. The forecast number of failures was calculated for the various components of the steering system and the grey system accuracy was assessed. This is aimed at finding out how useful this theory is for forecasting the number of steering system failures.

SOFTWARE RELIABILITY MODELING BASED ON MIXED POISSON DISTRIBUTIONS

2008 ◽  
Vol 15 (01) ◽  
pp. 19-32 ◽  
Author(s):  
HIROYUKI OKAMURA ◽  
TADASHI DOHI
Keyword(s):  
Poisson Process ◽  
Software Reliability ◽  
Estimation Method ◽  
Modeling Framework ◽  
Reliability Modeling ◽  
Reliability Models ◽  
Software Reliability Models ◽  
Mixed Poisson Process ◽  
The Difference ◽  
Non Homogeneous Poisson Process

This paper considers a novel modeling framework of software reliability models (SRMs). The proposed SRMs are based on the mixed Poisson distribution (MPD), which can involve the non-homogeneous Poisson process (NHPP) based SRMs completely, but are not always equivalent to them. More precisely, the MPD-based SRMs provide a mixture of NHPPs, and their statistical properties follows the mixed Poisson process. We develop a parameter estimation method for the MPD-based SRMs based on EM algorithm. In numerical examples, we mainly investigate the difference between conventional NHPP-based SRMs and MPD-based SRMs in the viewpoints of estimating parameters and software reliability.

Reliability Evaluation for Distribution System Including Protection System Failures

2014 ◽  
Vol 989-994 ◽  
pp. 1181-1184
Author(s):  
Zhan Long Zhang ◽  
Li Yao ◽  
Qun Wang
Keyword(s):  
Markov Model ◽  
System Reliability ◽  
Distribution System ◽  
Reliability Evaluation ◽  
Protection System ◽  
Test Results ◽  
Reliability Modeling ◽  
System Failures ◽  
Load Point ◽  
Distribution System Reliability

A new model and method for incorporating the effect of protection system failures into distribution system reliability evaluation are developed this paper. Firstly, the distribution system is partitioned into zones with the auto (or non-auto) switch being the boundary; through the network equivalent approach, for each load point, three component groups are respectively formed by two types of zone-elements. Then two types of protection failures and their impacts on reliability modeling are discussed. Finally, the reliability Markov model of load point with protection failures has been developed. The test results of the samples demonstrate the practicability and validness of the method.

The Development of Reliability Modeling and Analysis Tool Based on Stochastic Petri Nets

2010 ◽  
Vol 118-120 ◽  
pp. 566-570
Author(s):  
Wei Ping Wang ◽  
Shi Yi Bao ◽  
Zeng Liang Gao
Keyword(s):  
System Reliability ◽  
General Structure ◽  
Transient State ◽  
Software Tool ◽  
Stochastic Petri Nets ◽  
Analysis Tool ◽  
Unified Framework ◽  
Reliability Modeling ◽  
Modeling And Analysis ◽  
Reliability Models

Given the existing difficulties in conventional reliability models and the limitations of the current SPN software tools in terms of modeling system reliability, a software tool for modeling system reliability based on SPN named RelSPN@zer is developed, describing both the general structure and the underlying numerical methods of the tool. RelSPN@zer provides a unified framework for the modeling and evaluation of SPN running under MATLAB environment and is especially tailed to the system reliability analysis. Many metrics of system reliability can be obtained both under stationary and transient state. An example is given to illustrate the use of this package.

Reliability Prediction

2016 ◽  
pp. 120-158
Keyword(s):  
Block Diagram ◽  
Remaining Useful Life ◽  
System Level ◽  
Reliability Prediction ◽  
Component Level ◽  
Reliability Modeling ◽  
Reliability Models ◽  
Development Cycle ◽  
Useful Life

It is important to have an expectation about useful life of a system before its construction or even its remaining useful life during its operation. Reliability prediction is a tool for this goal. Reliability is the probability of performing adequately to achieve the desired aim of the system. In this chapter, probability calculation is used to predict failure rate of the converter. The formulation of these calculations are based on the concepts of failure factors which were described in the previous chapter. Some detailed examples are presented to show the power of probability tool for analyzing the behavior of complex systems. This chapter covers the methods for reliability calculation from component to system level. Some standards of reliability are presented. One can use the information from a reliability prediction to guide design decisions throughout the development cycle. MIL-HDBK-217 is described in details as a well-known standard for reliability prediction at component level. Reliability modeling is introduced for calculating the reliability at system level. Difference between system block diagram and reliability model is presented. The reliability models of various static and rotary power converters are expressed. Some examples are presented to demonstrate the procedure of calculations for a simple converter with its auxiliary components. This chapter gives a quantitative view to reader about evaluation of reliability and it can be used in the next chapters for reliability improvement.

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