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Research Article Open Access
The software industry has become one of the world's key industries in recent decades. The tremendous amount of growth in software development industry has taken a pace and has become a driving force. It has grabbed the attention of researchers due to its subtle impact on world's economy and society. Software engineering deals with the design and development of high quality and reliable software. The overall objective in developing software is to provide high quality software without errors and failures. In order to produce high quality software which confirms to be the requirement specifications, it is necessary to test the software. Testing is required to make the software error free.This paper also highlights different techniques used for test case generation. Multi-objective formulations are realistic models for many complex engineering optimization problems. Customized genetic algorithms have been demonstrated to be particularly effective to determine excellent solutions to these problems. In many real-life problems, objectives under consideration conflict with each other, and optimizing a particular solution with respect to a single objective can result in unacceptable results with respect to the other objectives. A reasonable solution to a multiobjective problem is to investigate a set of solutions, each of which satisfies the objectives at an acceptable level without being dominated by any other solution. This paper describes a method using multi-objective evolutionary algorithm for the automatic generation of test cases.
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Author(s): Sukhjinder Kaur
Random,cyclomatic complexity, fitness factor, multi-objective genetic algorithm, test data., Adaptive,Advanced Computing Architectures,Agent-Based Middleware,Calm Technology,CDMA/GSM Communication Protocol,Radar technology,Grid Computing,Database Security.