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Xuemei Zhang
Researcher at Rutgers University
Publications - 17
Citations - 1487
Xuemei Zhang is an academic researcher from Rutgers University. The author has contributed to research in topics: Software quality & Software reliability testing. The author has an hindex of 16, co-authored 17 publications receiving 1369 citations. Previous affiliations of Xuemei Zhang include Alcatel-Lucent.
Papers
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Journal ArticleDOI
NHPP software reliability and cost models with testing coverage
Hoang Pham,Xuemei Zhang +1 more
TL;DR: In this article, a software reliability model that incorporates testing coverage information is proposed, which is based on a nonhomogeneous Poisson process (NHPP) and can be used to estimate and predict the reliability of software products quantitatively.
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Considering fault removal efficiency in software reliability assessment
TL;DR: In this paper, imperfect debugging is considered in the sense that new faults can be introduced into the software during debugging and the detected faults may not be removed completely.
Journal ArticleDOI
An analysis of factors affecting software reliability
Xuemei Zhang,Hoang Pham +1 more
TL;DR: The findings of empirical research from 13 companies participating in software development are presented to identify the factors that may impact software reliability and provide a general guide to the important aspects to consider in the whole software development process.
Journal ArticleDOI
A software cost model with warranty and risk costs
Hoang Pham,Xuemei Zhang +1 more
TL;DR: In this article, a software reliability model based on non-homogeneous Poisson process is used to minimize the expected total software cost and a software tool is also developed using Excel and Visual Basic to facilitate the task of determining the optimal software release time.
Journal ArticleDOI
An NHPP Software Reliability Model and Its Comparison
Hoang Pham,Xuemei Zhang +1 more
TL;DR: A new model based on NHPP is presented and it can be shown that for the failure data used here, the new model fits and predicts much better than the existing models.