Machine Learning Approach for Software Reliability Growth Modeling with Infinite Testing Effort Function
Joint Authors
Ramasamy, Subburaj
Lakshmanan, Indhurani
Source
Mathematical Problems in Engineering
Issue
Vol. 2017, Issue 2017 (31 Dec. 2017), pp.1-6, 6 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2017-07-26
Country of Publication
Egypt
No. of Pages
6
Main Subjects
Abstract EN
Reliability is one of the quantifiable software quality attributes.
Software Reliability Growth Models (SRGMs) are used to assess the reliability achieved at different times of testing.
Traditional time-based SRGMs may not be accurate enough in all situations where test effort varies with time.
To overcome this lacuna, test effort was used instead of time in SRGMs.
In the past, finite test effort functions were proposed, which may not be realistic as, at infinite testing time, test effort will be infinite.
Hence in this paper, we propose an infinite test effort function in conjunction with a classical Nonhomogeneous Poisson Process (NHPP) model.
We use Artificial Neural Network (ANN) for training the proposed model with software failure data.
Here it is possible to get a large set of weights for the same model to describe the past failure data equally well.
We use machine learning approach to select the appropriate set of weights for the model which will describe both the past and the future data well.
We compare the performance of the proposed model with existing model using practical software failure data sets.
The proposed log-power TEF based SRGM describes all types of failure data equally well and also improves the accuracy of parameter estimation more than existing TEF and can be used for software release time determination as well.
American Psychological Association (APA)
Ramasamy, Subburaj& Lakshmanan, Indhurani. 2017. Machine Learning Approach for Software Reliability Growth Modeling with Infinite Testing Effort Function. Mathematical Problems in Engineering،Vol. 2017, no. 2017, pp.1-6.
https://search.emarefa.net/detail/BIM-1192114
Modern Language Association (MLA)
Ramasamy, Subburaj& Lakshmanan, Indhurani. Machine Learning Approach for Software Reliability Growth Modeling with Infinite Testing Effort Function. Mathematical Problems in Engineering No. 2017 (2017), pp.1-6.
https://search.emarefa.net/detail/BIM-1192114
American Medical Association (AMA)
Ramasamy, Subburaj& Lakshmanan, Indhurani. Machine Learning Approach for Software Reliability Growth Modeling with Infinite Testing Effort Function. Mathematical Problems in Engineering. 2017. Vol. 2017, no. 2017, pp.1-6.
https://search.emarefa.net/detail/BIM-1192114
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1192114