Discrete Nonhomogeneous Poisson Process Software Reliability Growth Models Based on Test Coverage

Wang, Shuanqi; Wu, Yumei; Lu, Minyan; Li, Haifeng

doi:10.1002/qre.1301

Cited by 26 publications

(20 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Most of the discrete‐time nonhomogeneous Poisson process‐based software reliability models proposed in the software reliability engineering literature were developed under the assumption that similar debugging effort is required for removing all the faults . However, this assumption may not be true in practice; as different faults may require different amount of debugging effort for their removal from the system.…”

Section: Software Reliability Modellingmentioning

confidence: 99%

An Integrated Framework for Developing Discrete‐Time Modelling in Software Reliability Engineering

Shatnawi

2016

Quality & Reliability Eng

View full text Add to dashboard Cite

In the software reliability engineering literature, few attempts have been made to study the fault debugging environment using discrete-time modelling. Most endeavours assume that a detected fault to have been either immediately removed or is perfectly debugged. Such discrete-time models may be used for any debugging environment and may be termed blackbox, which are used without having prior knowledge about the nature of the fault being debugged. However, if one has to develop a white-box model, one needs to be cognizant of the debugging environment. During debugging, there are numerous factors that affect the debugging process. These factors may include the internal, for example, fault density, and fault debugging complexity and the external that originates in the debugging environment itself, such as the skills of the debugging team and the debugging effort expenditures. Hence, the debugging environment fault removal may take a longer time after having been detected. Therefore, it is imperative to clearly understand the testing and debugging environment and, hence, the urgency to develop a model. The model ought to take into account the fault debugging complexity and incorporate the learning phenomenon of the debugger under imperfect debugging environment. This objective dictates developing a framework through an integrated modelling approach based on nonhomogenous Poisson process that incorporates these realistic factors during the fault debugging process. Actual software reliability data have been used to demonstrate applicability of the proposed integrated framework.

show abstract

Section: Software Reliability Modellingmentioning

confidence: 99%

An Integrated Framework for Developing Discrete‐Time Modelling in Software Reliability Engineering

Shatnawi

2016

Quality & Reliability Eng

View full text Add to dashboard Cite

show abstract

“…Finite results are developed, and then extended to handle the limiting case for complex systems. From the distribution in (4), the resulting maximum likelihood equations for estimating the gamma parameters are given as shown in (22)-(23) at the bottom of the page, where is defined as (24) Results analogous to those in (22) and (23) can be found by taking the limit with respect to . Setting the equal to in (25) and (26) will provide results equivalent to those in [18].…”

Section: Empirical Bayes Estimatorsmentioning

confidence: 99%

“…Okamura, et al [23] used the Expectation-Maximization (EM) algorithm to estimate the parameters in a NHPP software reliability growth model with log-normal failure times. Wang, et al [24] developed a discrete NHPP model for software reliability growth that considers test coverage. A Beta function based test coverage function was used in combination with an underlying discrete NHPP to develop a joint model that considers the improvement in reliability from perfect debugging.…”

mentioning

confidence: 99%

A Bayesian Model for Complex System Reliability Growth Under Arbitrary Corrective Actions

Wayne

Modarres²

2015

IEEE Trans. Rel.

View full text Add to dashboard Cite

This paper presents a new method for projecting the reliability growth of a complex continuously operating system. The model allows for arbitrary corrective action strategies, and it differs from other models of this type by using all available data rather than failure mode first occurrence times only. It also differs from other reliability growth projection models in that it provides a complete inference framework via the posterior distribution on the system failure intensity. A unique feature of this approach relative to other Bayesian techniques is the analytic expression for the failure intensity contribution from unobserved failure modes. Expressions for estimating the initial failure intensity, growth potential failure intensity, and the cumulative number of failure modes expected in future testing are also developed. Extensions to the basic framework are also developed. The first accounts for multiple systems under test, and the second develops the posterior distribution while allowing for uncertainty on the Fix Effectiveness Factor values that are assessed. Two separate goodness-of-fit procedures are presented for assessing the appropriateness of the underlying model assumptions.Index Terms-Bayesian reliability, fix effectiveness, reliability growth, reliability growth projection.

show abstract

“…While the CTMCbased SRMs with a finite number of software fault contents involve an integer-valued parameter, the NHPP-based SRMs can treat the real-valued parameters more easily and provide higher goodness-of-fit performance than the CTMC-based SRMs. If one focuses on the discrete-time NHPP-based SRMs, several parametric models have already been developed in [19], [28], [32], [33]. However, it is worth mentioning that the common NHPP-based SRMs are based on only the fault count data and are independent of software test metrics observed in software development processes.…”

Section: Introductionmentioning

confidence: 99%

Generalized Cox Proportional Hazards Regression-Based Software Reliability Modeling with Metrics Data

Kuwa

2013

2013 IEEE 19th Pacific Rim International Symposium on Dependable Computing

View full text Add to dashboard Cite

Multifactor software reliability modeling with software test metrics data is well known to be useful for predicting the software reliability with higher accuracy, because it utilizes not only software fault count data but also software testing metrics data observed in the development process. In this paper we generalize the existing Cox proportional hazards regression-based software reliability model by introducing more generalized hazards representation, and improve the goodness-of-fit and predictive performances. In numerical examples with real software development project data, we show that our generalized model can significantly outperform several logistic regression-based models as well as the existing Cox proportional hazards regression-based model.

show abstract

Discrete Nonhomogeneous Poisson Process Software Reliability Growth Models Based on Test Coverage

Cited by 26 publications

References 20 publications

An Integrated Framework for Developing Discrete‐Time Modelling in Software Reliability Engineering

An Integrated Framework for Developing Discrete‐Time Modelling in Software Reliability Engineering

A Bayesian Model for Complex System Reliability Growth Under Arbitrary Corrective Actions

Generalized Cox Proportional Hazards Regression-Based Software Reliability Modeling with Metrics Data

Contact Info

Product

Resources

About