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首页> 外文期刊>Software Testing, Verification and Reliability >Entropy based enhanced particle swarm optimization on multi-objective software reliability modelling for optimal testing resources allocation
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Entropy based enhanced particle swarm optimization on multi-objective software reliability modelling for optimal testing resources allocation

机译：基于熵的增强粒子群优化对多目标软件可靠性建模，以获得最优测试资源分配

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This paper proposes a generalization of the exponential software reliability model to characterize several factors including fault introduction and time-varying fault detection rate. The software life cycle is designed based on module structure such as testing effort spent during module testing and detected software faults etc. The resource allocation problem is a critical phase in the testing stage of software reliability modelling. It is required to make decisions for optimal resource allocation among the modules to achieve the desired level of reliability. We formulate a multi-objective software reliability model of testing resources for a new generalized exponential reliability function to characterizes dynamic allocation of total expected cost and testing effort. An enhanced particle swarm optimization (EPSO) is proposed to maximize software reliability and minimize allocation cost. We perform experiments with randomly generated testing-resource sets and varying the performance using the entropy function. The multi-objective model is compared with modules according to weighted cost function and testing effort measures in a typical modular testing environment.
机译：本文提出了指数软件可靠性模型的概括，以表征包括故障引入和时变故障检测率的几个因素。软件生命周期基于模块结构设计，例如模块测试期间花费的测试工作，并检测到软件故障等。资源分配问题是软件可靠性建模的测试阶段的关键阶段。需要做出决定，以实现模块之间的最佳资源分配，以实现所需的可靠性水平。我们制定了一种多目标软件可靠性模型，用于测试资源，以实现新的广义指数可靠性功能，以表征总预期成本和测试工作的动态分配。提出了一种增强的粒子群优化（EPSO）以最大化软件可靠性并最小化分配成本。我们使用随机生成的测试资源集进行实验并使用熵函数改变性能。根据加权成本函数和测试工作量在典型的模块化测试环境中的测试中将多目标模型进行比较。

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