Editorial: Verification, reliability and performance
Robert M. Hierons, Tao Xie · Software Testing Verification and Reliability · 2021
This issue includes three papers, covering software verification, software reliability modelling and performance assessment, respectively. The first paper, ‘Verification algebra for multi-tenant applications in VaaS architecture’, by Kai Hu, Ji Wan, Kan Luo, Yuzhuang Xu, Zijing Cheng and Wei-Tek Tsai, concerns verification in multi-tenant architectures. Multi-tenant architectures support composition of services and so the rapid development of applications. The issue addressed is the potentially massive number of possible applications formed by composing a given set of services. The authors propose a verification algebra that can determine the results of verification of new combinations of property/application on the basis of different combinations of services already verified and/or the verification of different, but related, properties. The overall approach was evaluated through simulations. (Recommended by Professor Paul Strooper) The second paper, ‘Entropy based enhanced particle swarm optimization on multi-objective software reliability modelling for optimal testing resources allocation’, by Pooja Rani and G. S. Mahapatra, concerns the optimum resource allocation problem to obtain the maximum reliability and minimum total cost under the testing effort constraint. The authors formulate a multi-objective software reliability model of testing resources for a new generalized exponential reliability function to characterize dynamic allocation of total expected cost and testing effort. The authors further propose an enhanced particle swarm optimization (EPSO) to maximize software reliability and minimize allocation cost. The authors conduct experiments to demonstrate the potential of the proposed approach to predict software reliability with greater accuracy. (Recommended by Professor Moonzoo Kim) The third paper, ‘Performance assessment based on stochastic differential equation and effort data for edge computing’, by Yoshinobu Tamura and Shigeru Yamada, concerns performance assessment based on the relationship between the cloud and edge services operated by using open-source software. The authors propose a two-dimensional stochastic differential equation model that considers the unique features with uncertainty from big data under the operation of cloud and edge services. The authors analyse actual data to show numerical examples of performance assessments considering the network connectivity as characteristics of cloud and edge services and compare the noise terms of the proposed model for actual data. (Recommended by Professor Min Xie)