Vol. 10 Issue 2
Year:2015
Issue:Oct-Dec
Title: Test Data Generator to Improve Code Coverage in Object Oriented Programming
Author Name:R.E. Harish Goud, C. Kishore and A. Srinivasulu
Synopsis:
The features of Object Oriented Programming (i.e., abstraction, encapsulation and visibility) prevent the direct access to some modules of the source code, so that the automated test data generation becomes a challenging task. To solve this problem, Search Based Software Testing (SBST) has been applied. Previously, Random search approach has been applied to generate test suite which achieves code coverage of 70% in less than 10 sec. To address the same problem, new search approach algorithms are used which generates a test suite of high code coverage than early approaches in less search time. The proposed approach, first describes how to structure the test data generation problem for unit-testing. Based on static analysis, it considers methods or constructors to change the state that may reach to test the target. After that, it introduces a generator of instance of classes using two strategies to increase the likelihood to reach the test target, such as Seeding and Diversification, which may produce test suite of high code coverage with less search time.
Year:2015
Issue:Oct-Dec
Title: Test Data Generator to Improve Code Coverage in Object Oriented Programming
Author Name:R.E. Harish Goud, C. Kishore and A. Srinivasulu
Synopsis:
The features of Object Oriented Programming (i.e., abstraction, encapsulation and visibility) prevent the direct access to some modules of the source code, so that the automated test data generation becomes a challenging task. To solve this problem, Search Based Software Testing (SBST) has been applied. Previously, Random search approach has been applied to generate test suite which achieves code coverage of 70% in less than 10 sec. To address the same problem, new search approach algorithms are used which generates a test suite of high code coverage than early approaches in less search time. The proposed approach, first describes how to structure the test data generation problem for unit-testing. Based on static analysis, it considers methods or constructors to change the state that may reach to test the target. After that, it introduces a generator of instance of classes using two strategies to increase the likelihood to reach the test target, such as Seeding and Diversification, which may produce test suite of high code coverage with less search time.
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