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Quality improvement and optimization of test cases: a hybrid genetic algorithm based approach

Published: 11 May 2010 Publication History

Abstract

Software development organizations spend considerable portion of their budget and time in testing related activities. The effectiveness of the verification and validation process depends upon the number of errors found and rectified before releasing the software to the customer side. This in turn depends upon the quality of test cases generated. The solution is to choose the most important and effective test cases and removing the redundant and unnecessary ones; which in turn leads to test case optimization. To achieve test case optimization, this paper proposed a heuristics guided population based search approach namely Hybrid Genetic Algorithm (HGA) which combines the features of Genetic Algorithm (GA) and Local Search (LS) techniques to reduce the number of test cases by improving the quality of test cases during the solution generation process. Also, to evaluate the performance of the proposed approach, a comparative study is conducted with Genetic Algorithm and Bacteriologic Algorithm (BA) and concluded that, the proposed HGA based approach produces better results.

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Published In

cover image ACM SIGSOFT Software Engineering Notes
ACM SIGSOFT Software Engineering Notes  Volume 35, Issue 3
May 2010
151 pages
ISSN:0163-5948
DOI:10.1145/1764810
Issue’s Table of Contents

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 11 May 2010
Published in SIGSOFT Volume 35, Issue 3

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Author Tags

  1. bacteriologic algorithm (BA)
  2. genetic algorithm (GA)
  3. hybrid genetic algorithm (HGA)
  4. quality
  5. test case
  6. test case optimization

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  • (2023)A systematic review on search‐based test suite reductionIET Software10.1049/sfw2.1210417:2(93-136)Online publication date: 20-Feb-2023
  • (2022)A Novel Real Coded Genetic Algorithm for Software Mutation TestingSymmetry10.3390/sym1408152514:8(1525)Online publication date: 26-Jul-2022
  • (2022)Achieving Agility in Projects Through Hierarchical Divisive Clustering AlgorithmJournal of Electronic Testing10.1007/s10836-022-06024-938:5(471-479)Online publication date: 23-Sep-2022
  • (2019)An Insight Into Test Case Optimization: Ideas and Trends With Future PerspectivesIEEE Access10.1109/ACCESS.2019.28994717(22310-22327)Online publication date: 2019
  • (2019)A systematic mapping addressing Hyper-Heuristics within Search-based Software TestingInformation and Software Technology10.1016/j.infsof.2019.06.012114:C(176-189)Online publication date: 1-Oct-2019
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