A Genetic Algorithm-Based Approach for Test Case Prioritization

2019 ◽  
pp. 24-37 ◽  
Author(s):  
Getachew Mekuria Habtemariam ◽  
Sudhir Kumar Mohapatra
Keyword(s):  
Genetic Algorithm ◽  
Test Case ◽  
Test Case Prioritization

A Test Case Prioritization Genetic Algorithm Guided by the Hypervolume Indicator

2020 ◽  
Vol 46 (6) ◽  
pp. 674-696 ◽  
Author(s):  
Dario Di Nucci ◽  
Annibale Panichella ◽  
Andy Zaidman ◽  
Andrea De Lucia
Keyword(s):  
Genetic Algorithm ◽  
Test Case ◽  
Test Case Prioritization ◽  
Hypervolume Indicator

scholarly journals K-Step Crossover Method based on Genetic Algorithm for Test Suite Prioritization in Regression Testing

2021 ◽  
Vol 27 (2) ◽  
pp. 170-189
Author(s):  
P. K. Gupta
Keyword(s):  
Genetic Algorithm ◽  
Regression Testing ◽  
Test Suite ◽  
Test Case ◽  
Test Cases ◽  
Test Case Prioritization ◽  
Average Percentage ◽  
Code Coverage ◽  
Test Case Selection ◽  
Case Selection

Software is an integration of numerous programming modules  (e.g., functions, procedures, legacy system, reusable components, etc.) tested and combined to build the entire module. However, some undesired faults may occur due to a change in modules while performing validation and verification. Retesting of entire software is a costly affair in terms of money and time. Therefore, to avoid retesting of entire software, regression testing is performed. In regression testing, an earlier created test suite is used to retest the software system's modified module. Regression Testing works in three manners; minimizing test cases, selecting test cases, and prioritizing test cases. In this paper, a two-phase algorithm has been proposed that considers test case selection and test case prioritization technique for performing regression testing on several modules ranging from a smaller line of codes to huge line codes of procedural language. A textual based differencing algorithm has been implemented for test case selection. Program statements modified between two modules are used for textual differencing and utilized to identify test cases that affect modified program statements. In the next step, test case prioritization is implemented by applying the Genetic Algorithm for code/condition coverage. Genetic operators: Crossover and Mutation have been applied over the initial population (i.e. test cases), taking code/condition coverage as fitness criterion to provide a prioritized test suite. Prioritization algorithm can be applied over both original and reduced test suite depending upon the test suite's size or the need for accuracy. In the obtained results, the efficiency of the prioritization algorithms has been analyzed by the Average Percentage of Code Coverage (APCC) and Average Percentage of Code Coverage with cost (APCCc). A comparison of the proposed approach is also done with the previously proposed methods and it is observed that APCC & APCCc values achieve higher percentage values faster in the case of the prioritized test suite in contrast to the non-prioritized test suite.

Improved Annealing-Genetic Algorithm for Test Case Prioritization

2017 ◽  
Vol 36 (3) ◽  
pp. 705-732
Author(s):  
Zan Wang ◽  
Xiaobin Zhao ◽  
Yuguo Zou ◽  
Xue Yu ◽  
Zhenhua Wang
Keyword(s):  
Genetic Algorithm ◽  
Test Case ◽  
Test Case Prioritization
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