A study of operational and testing reliability in software reliability analysis

Yang, Bo; Xie, Min

doi:10.1016/s0951-8320(00)00069-7

Cited by 111 publications

(37 citation statements)

References 16 publications

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“…The failure rate [6] is another parameter of interest. As explained by [14], in the operational phase of software, there will be no modifications made on the software source code, thus the software failure rate is a constant. For electronic hardware, a constant failure rate is normally observed in the operational phase as well.…”

Section: Parametersmentioning

confidence: 99%

“…The time that the k:th software program is running on the i:th machine is The total working time for a hardware element has two parts: running software and transmitting data, thus T w (Hardware) = Σ Hardware T w (Software) = Σ Hardware T w (Communication) (14) which means the summation of the execution time of all software programs running on this hardware and the communication time of all channels going through this hardware. The working time for a data source can be calculated as the summation of all communication times that access the data on the data source.…”

Section: Parametersmentioning

confidence: 99%

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Reliability of Cloud Computing Services

Mishra¹

2014

IOSRJEN

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-Cloud computing is a recently developed new technology for complex systems with massive scale service sharing, which is different from the resource sharing of the grid computing systems. Despite the profound technical challenges involved, reliability is not, at its root, a technical problem, nor will merely technical solution be sufficient. Instead deep economic, political, and cultural adjustments will ultimately be required, along with a major, long-term commitment in each sphere to deploy the requisite technical solutions at scale. Nevertheless, technological advance and enablers have a clear role in supporting such change, and information technology (IT) is a natural bridge between technical and social solutions because it can offer improved communication and transparency for fostering the necessary economic, political, and cultural adjustments. Various types of failures are interleaved in the cloud computing environment, such as overflow failure, timeout failure, resource missing failure, network failure, hardware failure, software failure, and database failure. This paper systematically analyzes cloud computing and models the reliability of the cloud services. . It is a holistic approach that stretches from power to waste to purchasing to education and is a lifecycle management approach to the deployment of IT across an organization using Markov models, Queuing Theory and Graph Theory. I. INTRODUCTIONCloud computing as a technology is difficult to define because it is evolving without a clear start point and no clear prediction of its future course. The cloud technology seems to be in flax, hence it may be one of the foundations of the next generation of computing.It's built on a solid array of fundamental and proven technologies:  virtualization,  grid computing,  service oriented architectures,  distributed computing,  broadband networks,  browser as a platform,  free and open source software,  autonomic systems,  web application frameworks  Service level agreements.Host a variety of different workloads, including batch-style back-end jobs and interactive, user-facing applications. Allow workloads to be deployed and scaled out quickly through the rapid provisioning of virtual machines or physical machines. Support redundant, self-recovering, highly scalable programming models that allow workloads to recover from many unavoidable hardware/software failures. Monitor resource use in real time to enable rebalancing of allocations when needed Cloud computing environments support grid computing by quickly providing physical and virtual servers on which the grid applications can run. Cloud computing is different from but related with grid computing, utility computing and transparent computing. Grid computing [1] is a form of distributed computing whereby a "super and virtual computer" composed of a cluster of networked, loosely-coupled computers acts in concert to perform very large tasks. Grid computing involves dividing a large task into many smaller tasks that run in parallel on separate...

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Section: Parametersmentioning

confidence: 99%

Section: Parametersmentioning

confidence: 99%

Reliability of Cloud Computing Services

Mishra¹

2014

IOSRJEN

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“…Their effectiveness has been demonstrated on a large spectrum of problems in the reliability optimization field, such as resource management and task partition in grid systems [19]- [21], redundancy allocation [22], [23], reliability optimization of weighted voting systems [24], and OTRAPs [7]- [12], [15], [17], [18]. In particular, evolutionary algorithms have been reported to perform better than some other techniques on OTRAPs [7], [9]- [12], [15], [17], [18].…”

Section: Nsga-ii Nondominated Sorting Genetic Algorithm IImentioning

confidence: 99%

“…It is essentially a genetic algorithm that aims to solve a single-objective problem whose objective function is the weighted sum of the reliability and cost. The second algorithm was proposed by Yang and Xie [9], and thus is denoted as the Y-X algorithm. It considers only the software reliability, and ignores the cost.…”

Section: A Comparing Moeas With Single-objective Approachesmentioning

confidence: 99%

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Multi-Objective Approaches to Optimal Testing Resource Allocation in Modular Software Systems

2010

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Abstract-Software testing is an important issue in software engineering. As software systems become increasingly large and complex, the problem of how to optimally allocate the limited testing resource during the testing phase has become more important, and difficult. Traditional Optimal Testing Resource Allocation Problems (OTRAPs) involve seeking an optimal allocation of a limited amount of testing resource to a number of activities with respect to some objectives (e.g., reliability, or cost). We suggest solving OTRAPs with Multi-Objective Evolutionary Algorithms (MOEAs). Specifically, we formulate OTRAPs as two types of multi-objective problems. First, we consider the reliability of the system and the testing cost as two objectives. Second, the total testing resource consumed is also taken into account as the third objective. The advantages of MOEAs over state-of-the-art single objective approaches to OTRAPs will be shown through empirical studies. Our study has revealed that a well-known MOEA, namely Nondominated Sorting Genetic Algorithm II (NSGA-II), performs well on the first problem formulation, but fails on the second one. Hence, a Harmonic Distance Based Multi-Objective Evolutionary Algorithm (HaD-MOEA) is proposed and evaluated in this paper. Comprehensive experimental studies on both parallel-series, and star-structure modular software systems have shown the superiority of HaD-MOEA over NSGA-II for OTRAPs.Index Terms-Multi-objective evolutionary algorithm, parallelseries modular software system, software engineering, software reliability, software testing, star-structure modular software system. ACRONYMS

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Software Reliability Assurance in Practice

Mijumbi

Okumoto

Asthana

2019

Wiley Encyclopedia of Electrical and Electronics Engineering

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The increasing importance and complexity of software in communications systems necessitates an evolution in software reliability assurance practices. In this article, we discuss the past of software reliability growth modeling (SRGM) – an essential component of software reliability assurance – so as to motivate the need for an automated, dynamic, and cloud‐based approach for SRGM. We then propose an exponential model‐based algorithm for predicting software defects in late phases of software development. Moreover, we also propose an early defect prediction model (eDPM), which can be used during early stages of software development when traditional methods are not as accurate. We also discuss how these two methods can be integrated to result into an enhanced SRGM model. Finally, we also discuss a number of steps that can be followed to identify and quantify improvement actions to assure the delivery of reliable software. Throughout the article, we use examples from real, large‐scale telecommunications software projects to illustrate the implications and accuracy of the presented algorithms.

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A study of operational and testing reliability in software reliability analysis

Cited by 111 publications

References 16 publications

Reliability of Cloud Computing Services

Reliability of Cloud Computing Services

Multi-Objective Approaches to Optimal Testing Resource Allocation in Modular Software Systems

Software Reliability Assurance in Practice

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