NDLI: Pragmatic study of parametric decomposition models for estimating software reliability growth

Content Provider	IEEE Xplore Digital Library
Author	Chin-Yu Huang Jung-Hua Lo Sy-Yen Kuo
Copyright Year	1998
Description	Author affiliation: Dept. of Electr. Eng., Nat. Taiwan Univ., Taipei, Taiwan (Chin-Yu Huang)
Abstract	Numerous stochastic models for the software failure phenomenon based on Nonhomogeneous Poisson Process (NHPP) have been proposed in the last three decades (1968-98). Although these models are quite helpful for software developers and have been widely applied at industrial organizations or research centers, we still need to do more work on examining/estimating the parameters of existing software reliability growth models (SRGMs). We investigate and account for three possible trends of software fault detection phenomena during the testing phase: increasing, decreasing and steady state. We present empirical results from quantitative studies on evaluating the fault detection process and develop a valid time-variable fault detection rate model which has the inherent flexibility of capturing a wide range of possible fault detection trends. The applicability of the proposed model and the related methods of parametric decomposition are illustrated through several real data sets from different software projects. Our evaluation results show that the analytic parametric decomposition approach for SRGM have a fairly accurate prediction capability. In addition, the testing effort control problem based on the proposed model is also demonstrated.
Starting Page	111
Ending Page	123
File Size	207431
Page Count	13
File Format	PDF
ISBN	0818689919
ISSN	10719458
DOI	10.1109/ISSRE.1998.730861
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1998-11-04
Publisher Place	Germany
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Software reliability Software testing Fault detection Parameter estimation Programming Electrical capacitance tomography Predictive models Software systems Power system modeling Logistics
Content Type	Text
Resource Type	Article

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