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Technique for Early Reliability Prediction of Software Components Using Behaviour Models

View Article: PubMed Central - PubMed

ABSTRACT

Behaviour models are the most commonly used input for predicting the reliability of a software system at the early design stage. A component behaviour model reveals the structure and behaviour of the component during the execution of system-level functionalities. There are various challenges related to component reliability prediction at the early design stage based on behaviour models. For example, most of the current reliability techniques do not provide fine-grained sequential behaviour models of individual components and fail to consider the loop entry and exit points in the reliability computation. Moreover, some of the current techniques do not tackle the problem of operational data unavailability and the lack of analysis results that can be valuable for software architects at the early design stage. This paper proposes a reliability prediction technique that, pragmatically, synthesizes system behaviour in the form of a state machine, given a set of scenarios and corresponding constraints as input. The state machine is utilized as a base for generating the component-relevant operational data. The state machine is also used as a source for identifying the nodes and edges of a component probabilistic dependency graph (CPDG). Based on the CPDG, a stack-based algorithm is used to compute the reliability. The proposed technique is evaluated by a comparison with existing techniques and the application of sensitivity analysis to a robotic wheelchair system as a case study. The results indicate that the proposed technique is more relevant at the early design stage compared to existing works, and can provide a more realistic and meaningful prediction.

No MeSH data available.


Analysis of sensitivity to states’ reliability.
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pone.0163346.g011: Analysis of sensitivity to states’ reliability.

Mentions: Sensitivity analyses, which vary certain parameters of the behaviour model in order to perceive the effects on the prediction results, are an important and common way to gain more insights into the reliability characteristics of the component under study [2,16,39]. Using the CR_Estimate algorithm we investigated the variation in the reliability of the component as a function of the reliability of states. The graph in Fig 11 and the results in Table 3 show the impact of changing the state reliability with respect to the component reliability. In our analysis we maintained the reliability values of all the states assigned in our previous calculations and varied the reliability of one state at a time between 0 and 1.


Technique for Early Reliability Prediction of Software Components Using Behaviour Models
Analysis of sensitivity to states’ reliability.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC5036808&req=5

pone.0163346.g011: Analysis of sensitivity to states’ reliability.
Mentions: Sensitivity analyses, which vary certain parameters of the behaviour model in order to perceive the effects on the prediction results, are an important and common way to gain more insights into the reliability characteristics of the component under study [2,16,39]. Using the CR_Estimate algorithm we investigated the variation in the reliability of the component as a function of the reliability of states. The graph in Fig 11 and the results in Table 3 show the impact of changing the state reliability with respect to the component reliability. In our analysis we maintained the reliability values of all the states assigned in our previous calculations and varied the reliability of one state at a time between 0 and 1.

View Article: PubMed Central - PubMed

ABSTRACT

Behaviour models are the most commonly used input for predicting the reliability of a software system at the early design stage. A component behaviour model reveals the structure and behaviour of the component during the execution of system-level functionalities. There are various challenges related to component reliability prediction at the early design stage based on behaviour models. For example, most of the current reliability techniques do not provide fine-grained sequential behaviour models of individual components and fail to consider the loop entry and exit points in the reliability computation. Moreover, some of the current techniques do not tackle the problem of operational data unavailability and the lack of analysis results that can be valuable for software architects at the early design stage. This paper proposes a reliability prediction technique that, pragmatically, synthesizes system behaviour in the form of a state machine, given a set of scenarios and corresponding constraints as input. The state machine is utilized as a base for generating the component-relevant operational data. The state machine is also used as a source for identifying the nodes and edges of a component probabilistic dependency graph (CPDG). Based on the CPDG, a stack-based algorithm is used to compute the reliability. The proposed technique is evaluated by a comparison with existing techniques and the application of sensitivity analysis to a robotic wheelchair system as a case study. The results indicate that the proposed technique is more relevant at the early design stage compared to existing works, and can provide a more realistic and meaningful prediction.

No MeSH data available.