Software should be a product that can be used easily and accurately by the user and should be improved quickly and accurately when problems arise. In many software development projects, software requirements are frequently modified during the design or development phase, which tests the development of specific designers’ or developers’ capabilities. Software development environmental factors (SDEFs), such as differences in mutual work recognition among users, developers, and testers or knowledge differences, can hinder communication, which may lead to faulty development owing to erroneous job definition. Because the exact size and scope of the software cannot be calculated, the risk of excessive requirements, such as schedule, cost, and manpower, may increase. This study aims to investigate 32 SDEFs to examine the influence of factors affecting the reliability of software developed by Korean companies to identify factors with high influence and compare differences with previous studies. Moreover, we found whether any new SDEFs from the top 10 rankings only affected Korean companies, and also US companies included in previous studies. A factor analysis revealed several potential factors to identify the mutually independent characteristics of the factors. Through statistical analysis methods, the difference between the group means and the impact on improving the software reliability were found in Korean companies. These findings can provide useful benefits to software developers and managers working in countries with different or similar cultures and help increase working efficiency, i.e., work versus time investment and software reliability improvement.
The opportunistic maintenance of a k-out-of-n:G system is studied in this paper. In many applications, it spends less cost and time to perform maintenance on several components jointly than on each component separately. Based on this fact, two new (τ, T) opportunistic maintenance models with the consideration of reliability requirements are proposed. In these two models with two decision variables τ ≤ T, only minimal repairs are performed on failed components before time τ, and corrective maintenance of all failed components are combined with preventive maintenance (pm) of all functioning but deteriorated components after τ; if the system survives to time T without perfect maintenance, it will be subject to pm at time T. Considering maintenance time, asymptotic system cost rate and availability are derived. The results obtained generalize and unify some previous research in this area. Application to aircraft engine maintenance is presented.
In this paper, two models predicting mean time until next failure based on Bayesian approach are presented. Times between failures follow Weibull distributions with stochastically decreasing ordering on the hazard functions of successive failure time intervals, reflecting the tester's intent to improve the software quality with each corrective action. We apply the proposed models to actual software failure data and show they give better results under sum of square errors criteria as compared to previous Bayesian models and other existing times between failures models. Finally, we utilize likelihood ratios criterion to compare new model's predictive performance.
Abstract COVID-19 is caused by a coronavirus called SARS-CoV-2. Many countries around the world implemented their own policies and restrictions designed to limit the spread of Covid-19 in recent months. Businesses and schools transitioned into working and learning remotely. In the United States, many states were under strict orders to stay home at least in the month of April. In recent weeks, there are some significant changes related restrictions include social-distancing, reopening states, and staying-at-home orders. The United States surpassed 2 million coronavirus cases on Monday, June 15, 2020 less than five months after the first case was confirmed in the country. The virus has killed at least 115,000 people in the United States as of Monday, June 15, 2020, according to data from Johns Hopkins University. With the recent easing of coronavirus-related restrictions and changes on business and social activity such as stay-at-home, social distancing since late May 2020 hoping to restore economic and business activities, new Covid-19 outbreaks are on the rise in many states across the country. Some researchers expressed concern that the process of easing restrictions and relaxing stay-at-home orders too soon could quickly surge the number of infected Covid-19 cases as well as the death toll in the United States. Some of these increases, however, could be due to more testing sites in the communities while others may be are the results of easing restrictions due to recent reopening and changed policies, though the number of daily death toll does not appear to be going down in recent days due to Covid-19 in the U.S. This raises the challenging question: How can policy decision-makers and community leaders make the decision to implement public policies and restrictions and keep or lift staying-at-home orders of ongoing Covid-19 pandemic for their communities in a scientific way? In this study, we aim to develop models addressing the effects of recent Covid-19 related changes in the communities such as reopening states, practicing social-distancing, and staying-at-home orders. Our models account for the fact that changes to these policies which can lead to a surge of coronavius cases and deaths, especially in the United States. Specifically, in this paper we develop a novel generalized mathematical model and several explicit models considering the effects of recent reopening states, staying-at-home orders and social-distancing practice of different communities along with a set of selected indicators such as the total number of coronavirus recovered and new cases that can estimate the daily death toll and total number of deaths in the United States related to Covid-19 virus. We compare the modeling results among the developed models based on several existing criteria. The model also can be used to predict the number of death toll in Italy and the United Kingdom (UK). The results show very encouraging predictability for the proposed models in this study. The model predicts that 128,500 to 140,100 people in the United States will have died of Covid-19 by July 4, 2020. The model also predicts that between 137,900 and 154,000 people will have died of Covid-19 by July 31, and 148,500 to 169,700 will have died by the end of August 2020, as a result of the SARS-CoV-2 coronavirus that causes COVID-19 based on the Covid-19 death data available on June 13, 2020. The model also predicts that 34,900 to 37,200 people in Italy will have died of Covid-19 by July 4, and 36,900 to 40,400 people will have died by the end of August based on the data available on June 13, 2020. The model also predicts that between 43,500 and 46,700 people in the United Kingdom will have died of Covid-19 by July 4, and 48,700 to 51,900 people will have died by the end of August, as a result of the SARS-CoV-2 coronavirus that causes COVID-19 based on the data available on June 13, 2020. The model can serve as a framework to help policy makers a scientific approach in quantifying decision-makings related to Covid-19 affairs.
A distributed database system often replicates data across its servers to provide a fault-resistant application, which maximizes server availability. Various replication control protocols have been developed to ensure data consistency. In this paper, we develop optimal design methods for the quorum-consensus replication protocol, which (1) maximizes availability of the distributed database systems and (2) minimizes the total system cost by calculating the optimal read quorum and the optimal number of system servers. Several numerical examples and applications are provided to illustrate the results.
When software systems are introduced, these systems are used in field environments that are the same as or close to those used in the development-testing environments; however, they may also be used in many different locations that may differ from the environment in which they were developed and tested. As such, it is difficult to improve software reliability for a variety of reasons, such as a given environment, or a bug location in code. In this paper, we propose a new software reliability model that takes into account the uncertainty of operating environments. The explicit mean value function solution for the proposed model is presented. Examples are presented to illustrate the goodness of fit of the proposed model and several existing non-homogeneous Poisson process (NHPP) models and confidence intervals of all models based on two sets of failure data collected from software applications. The results show that the proposed model fits the data more closely than other existing NHPP models to a significant extent.
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