Analysis and assessment of software reliability modeling with preemptive priority queueing policy

Lin, Jhih-Sin; Huang, Chin‐Yu; Fang, Chih‐Chiang

doi:10.1016/j.jss.2022.111249

Cited by 9 publications

(18 citation statements)

References 37 publications

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“…In the past, some researchers once proposed to use Infinite Server Queueing (ISQ) and/or Finite Server Queueing (FSQ) approaches to model the FDP and the FCP [9][11][14] [15]. Basically, both ISQ and FSQ approaches are based on queuing theory to make projections on software reliability.…”

Section: B Queueing-based Software Reliability Modelingmentioning

confidence: 99%

“…In the past, Lin et al [9] once proposed a preemptive priority queueing (PPQ) model that considers both a finite number of debuggers and different priority levels. Inoue and Yamada [15] also proposed an ISQ model considering the time distribution of the fault-isolation process based on DSS models.…”

Section: B Queueing-based Software Reliability Modelingmentioning

confidence: 99%

“…Here we will show an alternative approach to modeling the software reliability. In addition to our proposed queuing-based model [9], we find that simulation procedures can also be used to investigate the software fault correction process and to estimate system performance measures such as the average debugging time and waiting time. Specifically speaking, the queueing-based simulation approaches can relax certain unreasonable assumptions which are common in model-based approaches.…”

Section: Simulation-based Framework For Software Reliability Estimationmentioning

confidence: 99%

“…Priority scheduling [18][19] is an operating system process scheduling algorithm and can be generally classified as preemptive or non-preemptive. Based on this concept, Lin and Huang once [9] proposed a preemptive priority queueing (PPQ) model that considers both a finite number of debuggers and different priority levels. In actuality, in addition to above model-based approaches, the discrete-event simulation offers an alternative to analytical models as it can represent the impact of different strategies that may be used during testing [4].…”

Section: Introductionmentioning

confidence: 99%

“…Based on our past studies [9], in this paper we further propose queueing-based simulation techniques and simulation procedures to analyze the behavior and reliability of the PPQ model. The proposed simulator can describe the process of fault correction in more detail and optimize various system parameters such as staffing level, average response time, and average waiting time.…”

Section: Introductionmentioning

confidence: 99%

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Queueing-Based Simulation for Software Reliability Analysis

Lin

Huang

2022

IEEE Access

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As modern software system is growing in size and complexity, the customer expectations for software quality have become higher. In the past, many software reliability growth models (SRGMs) were proposed and they are helped to evaluate the quality of developed software. It is worth noting that some of SRGMs can be used to model the fault detection process (FDP) and the fault correction process (FCP) through an infinite server queueing (ISQ) system or a finite server queueing (FSQ) system. However, it can also be found that most ISQ and FSQ models were developed on a first come first served basis. In this paper, we propose to use the queueing-based simulations to describe the behavior of FCP and assess the software reliability instead of using model-based approaches. Our proposed queueing-based simulation techniques and simulation procedures will be able to thoroughly investigate the FCP and easily provide system performance information estimated based on the staffing level, the average response time, and the average waiting time. Numerical examples based on three real failure data are given and discussed. Our experiments show that the proposed simulation procedures obtain a good prediction capability for software reliability. We expect that the proposed methods can provide effective information for software developing management and help decision makers in resource allocation and cost control.

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Section: B Queueing-based Software Reliability Modelingmentioning

confidence: 99%

Section: B Queueing-based Software Reliability Modelingmentioning

confidence: 99%

Section: Simulation-based Framework For Software Reliability Estimationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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