DocumentCode
238104
Title
Parallelized ACO algorithm for regression testing prioritization in hadoop framework
Author
Elanthiraiyan, Nandhini ; Arumugam, Chamundeswari
Author_Institution
Dept. of Comput. Sci. & Eng., SSN Coll. of Eng., Chennai, India
fYear
2014
fDate
8-10 May 2014
Firstpage
1568
Lastpage
1571
Abstract
Regression testing is an important strategy in the software maintenance phase to produce a high quality software product. This testing ensures that the modified system code does not have an effect on the original software system. Initially, the test suite is generated for the existing software system. After the system undergoes changes the test suite contains both the original test cases and the modified test cases. Regression test prioritization method helps to separate the optimal test cases from the modified test suite. In the existing work, the multi-criteria optimization was applied for generating optimal regression test cases and it was carried out in a non-parallelized environment. The proposed solution is to extend the existing work by generating an optimized test suite using Ant Colony Optimization (ACO) technique on Hadoop Map reduce framework in a parallelized environment.
Keywords
optimisation; parallel processing; program testing; regression analysis; software maintenance; software quality; ACO; Hadoop Mapreduce framework; ant colony optimization technique; high quality software product; modified system code; multicriteria optimization; nonparallelized environment; optimal regression test cases; optimized test suite; parallelized ACO algorithm; parallelized environment; regression test prioritization method; software maintenance phase; test suite; Computer aided software engineering; Optimization; Software; TV; Ant Colony Optimization; Hadoop Mapreduce; Prioritization; Regression Testing; parallelization;
fLanguage
English
Publisher
ieee
Conference_Titel
Advanced Communication Control and Computing Technologies (ICACCCT), 2014 International Conference on
Conference_Location
Ramanathapuram
Print_ISBN
978-1-4799-3913-8
Type
conf
DOI
10.1109/ICACCCT.2014.7019371
Filename
7019371
Link To Document