Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/121691
Title: Development of Techniques and Models for Improving Software Reliability
Researcher: Pradeep Kumar
Guide(s): Yogesh Singh
University: Guru Gobind Singh Indraprastha University
Completed Date: 02/2014
Abstract: The purpose of this work is to develop the techniques and models for improving software reliability. This will lead to efficient design and development of quality software systems which can be delivered on time, within budget to the customers. A number of models are available in literature to predict software reliability. A structured review may provide commonalities and differences between predictability of these models. We thoroughly reviewed and compared various software reliability prediction models and metrics for the assessment and prediction of software reliability quantitatively. The findings may be used by software practitioners in later stages of software development to give a measurement of quality assessment. newline newlineIn this thesis, a non-homogeneous Poisson Process (NHPP) based software reliability growth model newlinefor three-tier client server system has been designed and developed. The proposed model has been newlinevalidated using standard failure dataset of seven commercial projects of public domain using newlinedifferent parameters like mean square error (MSE), mean absolute error (MAE), and R-values. The newlineproposed model specifically address the issue of when to release the software for operational use to newlinethe customers based on desired reliability achieved and optimized development cost. In order to newlinegeneralize results, an empirical study of software reliability growth model for three-tier client server newlinesystem has been conducted to improve predictability of the proposed model. Based on experimental results, it is observed that the proposed model is accurate in its prediction capacity having better newlinecapability of generalization. newlineThe findings of this work suggest that the proposed model and machine learning techniques can be used as effective tools for predicting software reliability accurately. newlineThe significance of this work can be used as guidelines for practitioners and industry professionals newlinein order to minimize the failure rate attaining maximum reliability, and cheaper software in a realistic operating context.
Pagination: 
URI: http://hdl.handle.net/10603/121691
Appears in Departments:University School of Information and Communication Technology

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01_coverpage.pdfAttached File25.33 kBAdobe PDFView/Open
02_certificate.pdf11.7 kBAdobe PDFView/Open
03_ dedicated to parents.pdf25.73 kBAdobe PDFView/Open
04_acknowledgement.pdf34.12 kBAdobe PDFView/Open
05_abstract.pdf10.42 kBAdobe PDFView/Open
06_toc.pdf24.03 kBAdobe PDFView/Open
07_figure.pdf17.39 kBAdobe PDFView/Open
08_tables.pdf15.61 kBAdobe PDFView/Open
09_abbreviation n symbols.pdf204.32 kBAdobe PDFView/Open
10_publications.pdf15.31 kBAdobe PDFView/Open
11_chapter_01.pdf149.13 kBAdobe PDFView/Open
12_chapter_02.pdf84.08 kBAdobe PDFView/Open
13_chapter_03.pdf115.25 kBAdobe PDFView/Open
14_chapter_04.pdf82.92 kBAdobe PDFView/Open
15_chapter_05.pdf238.38 kBAdobe PDFView/Open
16_chapter_06.pdf151.62 kBAdobe PDFView/Open
17_chapter_07.pdf241.16 kBAdobe PDFView/Open
18_chapter_08.pdf29.56 kBAdobe PDFView/Open
19_bibliography.pdf91.13 kBAdobe PDFView/Open


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