Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/38627
Title: Multi factor approach for effective Regression testing using test case Optimization
Researcher: Raju S
Guide(s): Uma G V
Keywords: Life cycle Regression
Regression testing
Software testing
Test case optimization
Upload Date: 6-Apr-2015
University: Anna University
Completed Date: 01/10/2014
Abstract: Software testing is a complex time consuming and expensive process newlinein the software development life cycle Regression testing is an important testing procedure used in validating modifications introduced in a system during software maintenance It is an expensive yet an important process As the test suite size is very large system retesting consumes large amount of time and computing resources Unfortunately there may not be sufficient resources to allow for the re execution of all test cases during regression testing Test case optimization using prioritization techniques aim to improve the effectiveness of newlineregression testing by re ordering the test cases so that the most beneficial test cases are executed at the earliest with higher priority newlineThe objective of test case prioritization is to detect faults as early as newlinepossible using a minimum number of test cases An attempt has been made in this research work for test case optimization by prioritization Three different approaches to prioritization are presented which are based on test case weights genetic algorithm and clustering techniques newlineTest case prioritization techniques organize the test cases in a test newlineSuite allowing for an increase in the effectiveness of testing A primary newlineperformance goal of the system the fault detection rate is a measure of how quickly faults are determined during the testing process newline
Pagination: xx, 182p.
URI: http://hdl.handle.net/10603/38627
Appears in Departments:Faculty of Information and Communication Engineering

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02_certificate.pdf790.23 kBAdobe PDFView/Open
03_abstract.pdf8.83 kBAdobe PDFView/Open
04_acknowledgement.pdf6.22 kBAdobe PDFView/Open
05_content.pdf25.59 kBAdobe PDFView/Open
06_chapter1.pdf159.06 kBAdobe PDFView/Open
07_chapter2.pdf96.07 kBAdobe PDFView/Open
08_chapter3.pdf90.82 kBAdobe PDFView/Open
09_chapter4.pdf81.26 kBAdobe PDFView/Open
10_chapter5.pdf879.84 kBAdobe PDFView/Open
11_chapter6.pdf930.62 kBAdobe PDFView/Open
12_chapter7.pdf247.2 kBAdobe PDFView/Open
13_chapter8.pdf24.55 kBAdobe PDFView/Open
14_appendix.pdf91.93 kBAdobe PDFView/Open
15_reference.pdf28.69 kBAdobe PDFView/Open
16_publication.pdf6.26 kBAdobe PDFView/Open


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