A flexible stochastic differential equation model in distributed development environment

Tamura, Yoshinobu; Yamada, Shigeru

doi:10.1016/j.ejor.2004.04.034

Cited by 64 publications

(17 citation statements)

References 5 publications

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“…Yamada et al (2003) further derived the SDE model for YDS model, Ohba (1984a) and DDE (Tamura and Yamada, 2006) models. Kapur et al (2007a) proposed a SDE model for multiple faults types with respect to time and testing efforts and also developed a unifying scheme for SDE based SRM.…”

Section: N(t) Is Treated Approximately As a Markov Process And Transfmentioning

confidence: 98%

Software quality assurance using software reliability growth modelling: state of the art

Kapur

Gupta

Jha

et al. 2010

IJBIS

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Computer systems have penetrated in every sphere of human life. It has made it necessary for the software developer to lay special emphasis on the quality of the software. Building quality software means having a right balance between the three major quality characteristics -reliability, development time and cost. Wealth of research in reliability modelling and applications is remarkable which confirms its continuing importance as a research topic.Among the various kind of reliability modelling non-homogeneous Poisson process (NHPP) based software reliability modelling has been a topic of most practical and academic interest. In this paper we review state-of-the-art of software reliability growth models (SRGM) based on NHPP and provide future directions.

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Section: N(t) Is Treated Approximately As a Markov Process And Transfmentioning

confidence: 98%

Software quality assurance using software reliability growth modelling: state of the art

Kapur

Gupta

Jha

et al. 2010

IJBIS

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“…Some researchers in the field of software reliability consider that variations in the mean value function stem from the error detection process, and deduce the mean value function by using stochastic differential equations (SDE) (Lee et al, 2004;Tamura and Yamada, 2006). Although SDE is recognized as a more effective method in evaluating the mean value function and the software testing costs, the existing models still have some problems.…”

Section: Literatures Reviewmentioning

confidence: 99%

“…Lee et al (2004) developed an SRGM by using an Itô type SDE based on the delayed S-shaped and the inflection S-shaped models proposed by Yamada et al (1983) and Ohba (1984), respectively, without considering the variation in mean value function, which is a crucial factor for measuring testing costs and the variation in software reliability during the testing phase. Tamura and Yamada (2006) derived a flexible SDE model to describe the fault-detection process during the system testing phase of a distributed development environment by using an inflection S-shaped SRGM and an Itô type SDE (Karatsas and Shreve, 1997), but this model improperly presents the prediction and variance of the mean value function, and there is no complete process for obtaining the parameters in the model. Furthermore, the notion of learning as an explicit feedback process has found its way into many areas of the social and management sciences.…”

Section: Literatures Reviewmentioning

confidence: 99%

A Discussion of Software Reliability Growth Models with Time-Varying Learning Effects

Chiu¹

2013

AJSEA

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Over the last few decades, software reliability growth models (SRGM) has been developed to predict software reliability in the testing/debugging phase. Most of the models are based on the Non-Homogeneous Poisson Process (NHPP), and an S or exponential-shaped type of testing behavior is usually assumed. Chiu et al. (2008) provided an SRGM that considers learning effects, which is able to reasonably describe the S and exponential-shaped behaviors simultaneously. This paper considers both linear and exponential-learning effects in an SRGM to enhance the model in Chiu et al. (2008), assumes the learning effects depend on the testing-time, and discusses when and what learning effects would occur in the software development process. This research also verifies the effectiveness of the proposed models with R square (Rsq), and compares the results with these of other models by using four real datasets. The proposed models consider constant, linear, and exponential-learning effects simultaneously. The results reveal the proposed models fit the data better than other models, and that the learning effects occur in the software testing process. The results are helpful for the software testing/debugging managers to master the schedule of the projects, the performance of the programmers, and the reliability of the software system.

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“…“Prescriptive models” apply mathematics and optimization to designing verification strategies (eg, ). However, the mathematical model is limited to reflect only certain aspects of verification strategies.…”

Section: Introductionmentioning

confidence: 99%

Elemental patterns of verification strategies

Salado

Kannan

2019

Systems Engineering

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This paper proposes a set of seven elemental patterns of verification strategies. These patterns can be useful in modeling verification strategies in a wide range of engineered systems. They form the building blocks under which any verification strategy can be modeled. The patterns lead to a fundamental understanding of the interplay between system parameters and verification activities, as well as an understanding of the mechanisms by which verification evidence builds up. For each pattern, we provide a description and a few examples of its application. A few important theoretical properties of the corresponding set of patterns are discussed, such as belief update, inferential properties, and graph disconnection, as well as some practical guidance to be taken into account when applying them to authentic verification problems. These patterns are intended to be a useful tool for researchers, practitioners, and educators, by formalizing the application of Bayesian networks to verification problems, hence facilitating instruction and communication among verification engineers and with researchers from other domains, particularly statisticians and Bayesian analysts.

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A flexible stochastic differential equation model in distributed development environment

Cited by 64 publications

References 5 publications

Software quality assurance using software reliability growth modelling: state of the art

Software quality assurance using software reliability growth modelling: state of the art

A Discussion of Software Reliability Growth Models with Time-Varying Learning Effects

Elemental patterns of verification strategies

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