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Determination of Software Release Instant of Three-Tier Client Server Software System
Yogesh Singh , Pradeep Kumar
Pages - 51 - 62     |    Revised - 30-08-2010     |    Published - 30-10-2010
Volume - 1   Issue - 3    |    Publication Date - September / October 2010  Table of Contents
Software Reliability Growth Model, Optimal Release Policy, Three-tier Client server System
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.
CITED BY (5)  
1 Jaiswal, A., & Malhotra, R. (2016). Software Reliability Prediction Using Machine Learning Techniques. In Proceedings of Fifth International Conference on Soft Computing for Problem Solving (pp. 141-163). Springer Singapore.
2 Kumar, P., & Wahid, A. (2016). Performance Evaluation of Data Mining Techniques for Predicting Software Reliability. network, 17, 19.
3 Kumar, P., & Singh, Y. (2013). Comparative analysis of software reliability predictions using statistical and machine learning methods. International Journal of Intelligent Systems Technologies and Applications, 12(3-4), 230-253.
4 Kumar, P., & Singh, Y. (2012). An empirical study of software reliability prediction using machine learning techniques. International Journal of System Assurance Engineering and Management, 3(3), 194-208.
5 Kumar, P., & Singh, Y. (2012). Assessment of software testing time using soft computing techniques.Acm Sigsoft Software Engineering Notes, 37(1), 1-6.
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Dr. Yogesh Singh
Guru Gobind Singh University - India
Associate Professor Pradeep Kumar
ABES Engineering College , Ghaziabad - India

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