One of the most important and effort intensive activity of the entire software development process is software testing. The effort involved chiefly increases because of the need to obtain optimal test data out of the entire search space of the problem under testing. Software test data generation is one area that has seen tremendous research in terms of automation and optimization. Generating or identifying an optimal test set that satisfies a more robust adequacy criteria, like data flow testing, is still a challenging task. A number of heuristic and meta-heuristics like genetic algorithm (GA), Particle Swarm Optimization (PSO) have been applied to optimize the test data generation problem. GA, although more popular, has its own difficulties such as complex to implement and slow convergence rate. In this paper an Adaptive Particle Swarm Optimization (APSO) algorithm is applied to generate test data for data-flow dependencies of a program guided by a novel fitness function. Adaptive PSO is used because of its capability of balancing in exploration and exploitation. A new fitness function is designed based on the concepts of dominance relations, weighted branch distance for APSO to guide the search direction. A set of benchmark programs and four modules of Krishna Institute of Engineering and Technology (KIET), Enterprise resource planning (ERP) system were taken for the experimental analysis. The experimental results show that the proposed adaptive PSO based approach performed significantly better than random search, Genetic Algorithm and PSO in enhancing the convergence speed.
Recently, the life in Earth becomes turbulent with the worldwide s pread of novel coronavirus (COVID-19). This outbreak has been declared as a public health emergency in the level of international concern by world health organization (WHO). To reduce the spread of COVID19 entire world has adopted social distancing, where working and learning from home is the new normal for this new world. To sustain the economical revenue and business growth companies that radically move into cloud infrastructure to support employees, who work remotely. With the unprecedented growth of cloud, data breaches and cyber security takes a huge leap. Apart from big cloud vendor small cloud startups are getting huge leap currently. Starting from enterprise solution providers, cloud supports in education, e-commerce, and healthcare also. Hackers penetrating not only the cloud resources it also hampers the hosts and device connected with it. This paper discovers several security challenges due to the sudden use of cloud platforms without adequate precautions. The aim of this paper is to highlight these areas causing security breaches and propose generic preventive measures.
Developing organizations are spends lots of money to finding the Errors and bugs. In this article, an application of defects removal effectiveness to improve the software quality and fault prone analysis, methods are finding the solution of parameters in linear regression models with cost estimating method. It describes the approach of quantitative quality management through defect removal effectiveness and statistical process control of cost analysis with historical project data. Software quality is going continuously monetary benefit to perform well management planning, and achieve a new height. In this methodology, Software quality model can make timely predictions of reliability indications; it's enabling to improve software development processes by target reducing the estimated cost for software products and improve the techniques for more effectively and efficiently.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.