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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.


Impact of the iteration number on component reliability.
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pone.0163346.g010: Impact of the iteration number on component reliability.

Mentions: To compute the reliability of the component, we set the maximum iteration number of the algorithm to a large number (in order to reach the steady state probability). Based on the result shown in Table 2 and Fig 10, it can be seen that the component reliability gradually decreased as the iteration number increased. Thereafter, the component reliability became stable when the iteration number was>4. We can, therefore, report that the reliability at the beginning of the execution was 0.981540, while it decreased and became stable at a value of 0.980344. The iteration number indicates the number of the execution cycle. Based on this result, the reliability of the avoid-component was 0.980344 which refers to the steady state probability of not being in any failure state.


Technique for Early Reliability Prediction of Software Components Using Behaviour Models
Impact of the iteration number on component reliability.
© Copyright Policy
Related In: Results  -  Collection

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

pone.0163346.g010: Impact of the iteration number on component reliability.
Mentions: To compute the reliability of the component, we set the maximum iteration number of the algorithm to a large number (in order to reach the steady state probability). Based on the result shown in Table 2 and Fig 10, it can be seen that the component reliability gradually decreased as the iteration number increased. Thereafter, the component reliability became stable when the iteration number was>4. We can, therefore, report that the reliability at the beginning of the execution was 0.981540, while it decreased and became stable at a value of 0.980344. The iteration number indicates the number of the execution cycle. Based on this result, the reliability of the avoid-component was 0.980344 which refers to the steady state probability of not being in any failure state.

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.