Yoshinobu Tamura

Comparison of Software Reliability Assessment Methods for Open Source Software

IT (information technology) advanced with steady steps from 1970s is essential in our daily life. As the results of the advances in high-speed data-transfer network technology, software development environment has been changing into new development paradigm. In this paper, we propose software reliability assessment methods for concurrent distributed system development by using the analytic hierarchy process. Also, we make a comparison between the inflection S-shaped software reliability growth model and the other models based on a nonhomogeneous Poisson process applied to reliability assessment of the entire system composed of several software components. Moreover, we analyze actual software fault count data to show numerical examples of software reliability assessment for the open source project. Furthermore, we investigate an efficient software reliability assessment method for the actual open source system development

Optimisation analysis for reliability assessment based on stochastic differential equation modelling for open source software

A new distributed software development paradigm typified by such open source project will evolve at a rapid pace in the future. Especially, open source software (OSS) which serve as key components of critical infrastructures in the social life are still ever-expanding now. We propose a software reliability growth model based on stochastic differential equations in order to consider the active state of the open source project. Especially, we assume that the software failure intensity depends on the time, and the software fault-reporting phenomena on the bug-tracking system keeps an irregular state. Moreover, it has been necessary to manage the software development process in terms of reliability, development effort and version-upgrade time. We find the optimal version-upgrade time based on the total expected software maintenance effort. We show that the proposed model can assist improvement of quality for OSS systems developed under the open source project.

Software reliability assessment and optimal version-upgrade problem for Open Source Software

Network technologies have made rapid progress with the dissemination of computer systems in all areas. The current software development environment has been changing into new development paradigms such as concurrent distributed development environment and the so-called open source project by using network computing technologies. Especially, OSS (Open Source Software) systems which serve as key components of critical infrastructures in the society are still ever-expanding now. We focus on OSS developed under open source project. In case of considering the effect of the debugging process on an entire system in the development of a method of reliability assessment for open source project, it is necessary to grasp the deeply-intertwined factors, such as programming path, size of each component, skill of fault reporter, and so on. In order to consider the effect of each software component on the reliability of an entire system under such open source software development, we propose a new approach to software reliability assessment by creating a fusion of neural networks and a software reliability growth model. Also, it has been necessary to manage the software development process in terms of reliability, effort, and version-upgrade time. In this paper, we find the optimal total version-upgrade time based on the total expected software maintenance effort.

A Method of User-oriented Reliability Assessment for Open Source Software and Its Applications

Software development environment has been changing into new development paradigms such as concurrent distributed development environment and the so-called open source project by using network computing technologies. In case of considering the effect of the debugging process on an entire system in the development of a method of reliability assessment for open source project, it is necessary to grasp the deeply-intertwined factors, such as programming path, size of each component, skill of fault reporter, and so on. In order to consider the effect of each software component on the reliability of an entire system under such new distributed development paradigm, we propose a new approach to software reliability assessment by creating a fusion of neural network and software reliability growth model. In this paper, we show application examples of software reliability assessment based on neural network and software reliability growth model for open source software. Also, we analyze actual software fault count data to show numerical examples of software reliability assessment for the open source software. Then, we consider the efficiency and effectiveness of the software reliability assessment method for the actual open source software.

Reliability assessment based on hazard rate model for an embedded OSS porting-phase

Embedded Open Source Software (OSS) systems have been gaining a lot of attention in the embedded system area. The successful experience of embedded OSSs include Android, BusyBox, TRON, etc. Also, OSS market will be expanded not only to mobile phone OSS areas but also to other embedded OSS areas in the future. However, the poor handling of quality problem and customer support prohibit the progress of embedded OSS. Also, it is difficult for developers to assess the reliability and portability of embedded OSS on a single‐board computer. A method of software reliability assessment based on flexible hazard rate modeling for the embedded OSS is proposed in this paper. Also, several numerical examples are shown by using actual data. Moreover, this paper compares the proposed model with the conventional hazard rate models by using the comparison criteria of goodness‐of‐fit. Copyright

OSS Reliability Measurement and Assessment

This book analyses quantitative open source software (OSS) reliability assessment and its applications, focusing on three major topic areas: the Fundamentals of OSS Quality/Reliability Measurement and Assessment; the Practical Applications of OSS Reliability Modelling; and Recent Developments in OSS Reliability Modelling. Offering an ideal reference guide for graduate students and researchers in reliability for open source software (OSS) and modelling, the book introduces several methods of reliability assessment for OSS including component-oriented reliability analysis based on analytic hierarchy process (AHP), analytic network process (ANP), and non-homogeneous Poisson process (NHPP) models, the stochastic differential equation models and hazard rate models. These measurement and management technologies are essential to producing and maintaining quality/reliable systems using OSS.

Reliability analysis based on jump diffusion models for an open source cloud computing

A cloud computing is also attracting attention as a network service to share the computing resources such as networks, servers, storage, applications, and services. We focus on a cloud computing environment by using open source software such as OpenStack and Eucalyptus because of the unification management of data, and low cost. In this paper, we propose a new approach to software reliability assessment based on a jump diffusion model based on the stochastic differential equations in order to consider the interesting aspect of the numbers of components and users. Also, actual software fault-count data are analyzed in order to show numerical examples of software reliability assessment. Moreover, this paper shows that the proposed method of reliability analysis can assist quality improvement for the cloud computing.

Software Reliability Growth Model Based on Stochastic Differential Equations for Open Source Software

All over the world people can gain the information at the same time by growing rate of Internet access around the world in recent years. In accordance with such a penetration of the Internet, it is increasing public awareness of the importance of online real-time and interactive functions. Therefore, software development environment has been changing into new development paradigms such as concurrent distributed development environment and the so-called open source project by using network computing technologies. Especially, such OSS (open source software) systems which serve as key components of critical infrastructures in our society are still ever-expanding now. In this paper, we propose a software reliability growth model based on stochastic differential equations in order to consider the active state of the open source project. Especially, we assume that the software failure intensity depends on the time, and the software fault-report phenomena on the bug tracking system keep an irregular state. Also, we analyze actual software fault count data to show numerical examples of software reliability assessment for the OSS. Moreover, we compare our model with the conventional model based on stochastic differential equations in terms of goodness-of-fit for actual data. We show that the proposed model can assist improvement of quality for OSS systems developed under the open source project.

Reliability Analysis Based on a Jump Diffusion Model with Two Wiener Processes for Cloud Computing with Big Data

At present, many cloud services are managed by using open source software, such as OpenStack and Eucalyptus, because of the unification management of data, cost reduction, quick delivery and work savings. The operation phase of cloud computing has a unique feature, such as the provisioning processes, the network-based operation and the diversity of data, because the operation phase of cloud computing changes depending on many external factors. We propose a jump diffusion model with two-dimensional Wiener processes in order to consider the interesting aspects of the network traffic and big data on cloud computing. In particular, we assess the stability of cloud software by using the sample paths obtained from the jump diffusion model with two-dimensional Wiener processes. Moreover, we discuss the optimal maintenance problem based on the proposed jump diffusion model. Furthermore, we analyze actual data to show numerical examples of dependability optimization based on the software maintenance cost considering big data on cloud computing.

A COMPONENT-ORIENTED RELIABILITY ASSESSMENT METHOD FOR OPEN SOURCE SOFTWARE

Software development environment has been changing into new development paradigms such as concurrent distributed development environment and the so-called open source project by using network computing technologies. Especially, an OSS (open source software) system which serves as key components of critical infrastructures in the society is still ever-expanding now. In case of considering the effect of the debugging process on an entire system in the development of a method of reliability assessment for the OSS, it is necessary to grasp the deeply-intertwined factors, such as programming path, size of each component, skill of fault reporter, and so on. In order to consider the effect of each software component on the reliability of an entire system, we propose a new approach to user-oriented software reliability assessment by creating a fusion of neural network and software reliability growth modeling. In this paper, we show application examples of component-oriented software reliability assessment based on neural network and software reliability growth modeling for the OSS. Also, we analyze actual software fault count data to show numerical examples of software reliability assessment for the OSS. Moreover, we develop the testing management tool for OSS.