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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.


The CPDG of the avoid-component: (a) before and (b) after assigning the values of state reliablities and transtion probablities.
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pone.0163346.g009: The CPDG of the avoid-component: (a) before and (b) after assigning the values of state reliablities and transtion probablities.

Mentions: To prepare the CPDG of the avoid-component, two steps are needed: constructing the CPDG, and generating the operational data relevant to the component. The data are used to assign the values of the transition probabilities of the CPDG. The CPDG is constructed through mapping each state and transition in the behaviour model to the node and edge in the CPDG. The super nodes Entry and Exit are added to represent the instantiation and termination of execution. Fig 9(a) shows the constructed CPDG of the avoid-component.


Technique for Early Reliability Prediction of Software Components Using Behaviour Models
The CPDG of the avoid-component: (a) before and (b) after assigning the values of state reliablities and transtion probablities.
© Copyright Policy
Related In: Results  -  Collection

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

pone.0163346.g009: The CPDG of the avoid-component: (a) before and (b) after assigning the values of state reliablities and transtion probablities.
Mentions: To prepare the CPDG of the avoid-component, two steps are needed: constructing the CPDG, and generating the operational data relevant to the component. The data are used to assign the values of the transition probabilities of the CPDG. The CPDG is constructed through mapping each state and transition in the behaviour model to the node and edge in the CPDG. The super nodes Entry and Exit are added to represent the instantiation and termination of execution. Fig 9(a) shows the constructed CPDG of the avoid-component.

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.