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| Determination of Software Release Instant of Three-Tier Client Server Software System
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Full
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Source |
International Journal of Software Engineering (IJSE) |
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Table of Contents |
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Volume: 1 Issue: 3 |
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Pages: |
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Publication
Date: October 2010 |
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ISSN
(Online): 2180-1320 |
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Pages |
51 - 62 |
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Author(s) |
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Published
Date |
30-10-2010 |
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Publisher |
CSC
Journals, Kuala Lumpur,
Malaysia |
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ADDITIONAL
INFORMATION |
| Keywords Abstract References Cited by Related Articles Collaborative
Colleague |
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KEYWORDS: Software Reliability Growth Model, Optimal Release Policy, Three-tier Client server System |
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| Quality of any software system mainly depends on how much time testing take place, what kind of testing methodologies are used, how complex the software is, the amount of efforts put by software developers and the type of testing environment subject to the cost and time constraint. More time developers spend on testing more errors can be removed leading to better reliable software but then testing cost will also increase. On the contrary, if testing time is too short, software cost could be reduced provided the customers take risk of buying unreliable software. However, this will increase the cost during operational phase since it is more expensive to fix an error during operational phase than during testing phase. Therefore it is essentially important to decide when to stop testing and release the software to customers based on cost and reliability assessment. In this paper we present a mechanism of when to stop testing process and release the software to end-user by developing a software cost model with risk factor. Based on the proposed method we specifically address the issues of how to decide that we should stop testing and release the software based on three-tier client server architecture which would facilitates software developers to ensure on-time delivery of a software product meeting the criteria of achieving predefined level of reliability and minimizing the cost. A numerical example has been cited to illustrate the experimental results showing significant improvements over the conventional statistical models based on NHPP. |
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| Yogesh Singh : Colleagues
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| Pradeep Kumar : Colleagues
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