Reliability estimation of hierarchical software systems

Author(s):  
S.P. Leblanc ◽  
P.A. Roman
Author(s):  
Yaguang Yang

System safety is closely related to system reliability. Safety requirements many times are translated to reliability requirements. Nowadays, software systems exist in many engineering systems. However, there is no consensus method for software reliability estimation. On the contrary, there is an increasing interest in estimating the software reliability due to concerns for safety-critical systems. In this article, we try to close the gap by proposing a systematic and probabilistic method to estimate the software reliability based on software test data.


Author(s):  
Preeti Malik ◽  
Lata Nautiyal ◽  
Mangey Ram

Component-based software engineering has proved itself as a strong pillar in software engineering community. Large number of factors are involved in the success of software product developed using Component-based software engineering, for instance, security, reliability, quality, safety, and testability. As the component-based software is made up of large number of components put together, therefore components’ reliability influence the reliability of the complete software. Numerous models and principles have been established to estimate the reliability of software by applying information regarding architecture, quality and security attributes of the software. Many researchers overlooked a major factor while estimating reliability of component-based software and that is error-propagation. Although it can be a case that the components are not dependent on each other and they are supplemented with the wrappers. However it is not true for many component-based applications. In this paper, a framework for reliability estimation has been proposed. In our proposal we have considered error propagation. We have analyzed the program structure and also presented how they are handled in estimation process. Further sensitivity analysis is done to identify the most sensitive component of the system. A numerical simulation is also presented for better understanding of the proposed framework.


Author(s):  
R. Chinnaiyan ◽  
S. Somasundaram

This paper presents a graphical complexity measure based approach with an illustration for estimating the reliability of software component. This paper also elucidates how the graph-theory concepts are applied in the field of software programming. The control graphs of several actual software components are described and the correlation between intuitive complexity and the graph-theoretic complexity are illustrated. Several properties of the graph theoretic complexity are presented which shows that the software component complexity depends only on the decision structure. A symbolic reliability model for component based software systems from the execution path of software components connected in series, parallel or mixed configuration network structure is presented with a crisp narration of the factors which influence computation of the overall reliability of component based software systems. In this paper, reliability estimation model for software components using Component Execution Paths (CEP) based on graph theory is elucidated.


Author(s):  
Alberto Avritzer ◽  
Flávio P. Duarte ◽  
Rosa Maria Meri Leão ◽  
Edmundo de Souza e Silva ◽  
Michal Cohen ◽  
...  

Methodology ◽  
2012 ◽  
Vol 8 (2) ◽  
pp. 71-80 ◽  
Author(s):  
Juan Botella ◽  
Manuel Suero

In Reliability Generalization (RG) meta-analyses, the importance of bearing in mind the problems of range restriction or biased sampling and their influence on reliability estimation has often been highlighted. Nevertheless, the presence of heterogeneous variances in the included studies has been diagnosed in a subjective way and has not been taken into account in later analyses. Procedures to detect the presence of a variety of sampling schemes and to manage them in the analyses are proposed. The procedures are further explained with an example, by applying them to 25 estimates of Cronbach’s alpha coefficient in the Hamilton Scale for Depression.


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