A comparative study of regression testing methods. (c1996)

LAUR Repository

Show simple item record

dc.contributor.author Baradhi, Ghinwa S.
dc.date.accessioned 2010-12-13T07:05:21Z
dc.date.available 2010-12-13T07:05:21Z
dc.date.copyright 1996 en_US
dc.date.issued 2010-12-13
dc.date.submitted 1996-06
dc.identifier.uri http://hdl.handle.net/10725/148
dc.description Includes bibliographical references (l. 70-71). en_US
dc.description.abstract We present a comparative study of popular regressIOn testing algorithms. These algorithms include slicing, incremental, firewall, genetic, and simulated annealing algorithms. The study uses a variety of small-size and medium-size modules along with associated test cases tables, and is based on the following quantitative and qualitative criteria, efficiency, number of retests, precision, inclusiveness, user's parameter setting, global variables, type of maintenance, type of testing, level of testing, and type of approach. The comparison results show that the five algorithms are suitable for different requirements of regression testing. Slicing and adapted firewall algorithms detect the definition-use pairs that are affected by a change, and select the test cases for regression testing based on these definition-use pairs. Incremental algorithm selects the test cases whose outputs may be affected. Genetic and simulated annealing select the minimum number of test cases that provide full testing coverage. In terms of execution time for small-size modules, slicing, incremental, and adapted firewall algorithms exhibit a better behavior comparing to genetic and simulated annealing algorithms. For medium-size modules, the adapted firewall algorithm becomes the slowest. Genetic and simulated annealing algorithms produce the least number of retests, followed by incremental, slicing, and then adapted firewall. en_US
dc.language.iso en en_US
dc.subject Regression teaching methods -- Comparative studies en_US
dc.title A comparative study of regression testing methods. (c1996) en_US
dc.type Thesis en_US
dc.term.submitted Spring en_US
dc.author.degree MS in Computer Science en_US
dc.author.school Arts and Sciences en_US
dc.author.commembers Dr. Ale Hejase
dc.author.commembers Dr. Walid Keirouz
dc.author.woa RA en_US
dc.description.physdesc 1 bound copy: vii, 71 leaves ; ill., tables available at RNL. en_US
dc.author.division Computer Science en_US
dc.author.advisor Dr. Nashat Mansour
dc.identifier.doi https://doi.org/10.26756/th.1996.2 en_US
dc.publisher.institution Lebanese American University en_US
dc.author.affiliation Lebanese American University en_US

Files in this item

This item appears in the following Collection(s)

Show simple item record

Search LAUR

Advanced Search


My Account