Scalable Scenario Specifications to Synthesize Component-centric Behaviour Models

Ali, A. B. M. Shawkat; Jawawi, Dayang Norhayati Abang; Isa, Mohd Adham

doi:10.14257/ijseia.2015.9.9.08

Cited by 3 publications

(8 citation statements)

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“…The process of synthesizing the component behaviour from scenario specifications as a popular requirements elicitation tool involves three activities: preparing scenarios, translating the component instances in each scenario to FSMs, and merging the FSMs of each component into one state machine model such as the labelled transition system (LTS). In order to define how the behaviour models can be synthesized, this section briefly reviews our previous research work [ 26 ], which is relevant to the synthesis of behaviour models from requirements specification; noting that the technique proposed in this paper is not dependent only on our previous work. Any behaviour model that is obtained through one of the existing behaviour synthesis methods or even from a component’s code as a result of a reverse engineering process can be used.…”

Section: Proposed Technique For Component Reliability Predictionmentioning

confidence: 99%

Technique for Early Reliability Prediction of Software Components Using Behaviour Models

et al. 2016

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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.

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Section: Proposed Technique For Component Reliability Predictionmentioning

confidence: 99%

Technique for Early Reliability Prediction of Software Components Using Behaviour Models

et al. 2016

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“…As shown in Figure 1 , model activities start by describing system scenarios using s-TS [ 12 ]. The activities of phase one are illustrated using a run-time example in Section 3.1 .…”

Section: The Proposed Modelmentioning

confidence: 99%

“…Software behavior models that are derived from requirement specifications [ 8 , 9 , 10 , 11 , 12 ] are basic building blocks for design-time reliability predictions. Many studies have been carried out based on these behavior models to predict the reliability at early design stages [ 7 , 13 , 14 , 15 , 16 , 17 , 18 ].…”

Section: Introductionmentioning

confidence: 99%

“…The first part of this paper shows a strategy for scalable scenario modeling and reliability calculations. It adapts our previous scenario description language named scalable triggered scenario ( s-TS ), which was introduced in [ 12 ]. The s-TS s helps in describing scenarios of a system compactly and concisely, leading to fewer scenarios.…”

Section: Introductionmentioning

confidence: 99%

“…The s-TS s helps in describing scenarios of a system compactly and concisely, leading to fewer scenarios. The s-TS in our previous work [ 12 ] was used to obtain individual components’ behavior models, whereas in this paper, it is utilized for analysis of the complete behavior of the scenarios to predict the reliability of the scenarios and then the whole system. The first part of this paper also includes an approach toward improving scenarios’ traceability by avoiding computational complexity.…”

Section: Introductionmentioning

confidence: 99%

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Design-Time Reliability Prediction Model for Component-Based Software Systems

Ali

Bashir

Hassan

et al. 2022

Sensors

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Software reliability is prioritised as the most critical quality attribute. Reliability prediction models participate in the prevention of software failures which can cause vital events and disastrous consequences in safety-critical applications or even in businesses. Predicting reliability during design allows software developers to avoid potential design problems, which can otherwise result in reconstructing an entire system when discovered at later stages of the software development life-cycle. Several reliability models have been built to predict reliability during software development. However, several issues still exist in these models. Current models suffer from a scalability issue referred to as the modeling of large systems. The scalability solutions usually come at a high computational cost, requiring solutions. Secondly, consideration of the nature of concurrent applications in reliability prediction is another issue. We propose a reliability prediction model that enhances scalability by introducing a system-level scenario synthesis mechanism that mitigates complexity. Additionally, the proposed model supports modeling of the nature of concurrent applications through adaption of formal statistical distribution toward scenario combination. The proposed model was evaluated using sensors-based case studies. The experimental results show the effectiveness of the proposed model from the view of computational cost reduction compared to similar models. This reduction is the main parameter for scalability enhancement. In addition, the presented work can enable system developers to know up to which load their system will be reliable via observation of the reliability value in several running scenarios.

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