Modified Genetic Algorithm for Performing the Regression Testing
By: Dubey, Lata
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Contributor(s): Seema.
Publisher: New Delhi STM Journals 2019Edition: Vol.6(2), May-Aug.Description: 69-75p.Subject(s): Computer EngineeringOnline resources: Click here
In:
Journal of artificial intelligence research and advances (JoAIRA)Summary: It states simply what work Software Testing is an approach where deferent errors and bugs in the software are identified. To test software we need the test cases. One of the mainly imperative activities in software maintenance is Regression Testing. The re-execution of all test cases throughout the regression testing is expensive and time consuming. And even though several of the code based planned techniques by researchers address technical programs. In our research work we proposed a regression test case selection for optimizes the selected test case with Genetic Algorithm. We are executing genetic algorithm upon different crossover rates (CR) and analyzed the results on number of iterations. The test cases are automatically generated through path crawler tool. We have taken 100% path coverage of the given source code. The effectiveness of the approach was evaluated calculating Average Percentage of Modified Genetic Algorithm (MGA) over Simple Genetic Algorithm (SGA). Proposed Approach (PA) provides significantly improved outcome in term of average percentage.
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School of Engineering & Technology Archieval Section | Not for loan | 2020177 |
It states simply what work Software Testing is an approach where deferent errors and bugs in the software are identified. To test software we need the test cases. One of the mainly imperative activities in software maintenance is Regression Testing. The re-execution of all test cases throughout the regression testing is expensive and time consuming. And even though several of the code based planned techniques by researchers address technical programs. In our research work we proposed a regression test case selection for optimizes the selected test case with Genetic Algorithm. We are executing genetic algorithm upon different crossover rates (CR) and analyzed the results on number of iterations. The test cases are automatically generated through path crawler tool. We have taken 100% path coverage of the given source code. The effectiveness of the approach was evaluated calculating Average Percentage of Modified Genetic Algorithm (MGA) over Simple Genetic Algorithm (SGA). Proposed Approach (PA) provides significantly improved outcome in term of average percentage.
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