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Retesting of software is done frequently during the software development life cycle and in particular in regression testing. In regression testing software grows and evolves that create new test cases and added them to a test suite to exercise the latest changes in the software[5]. Due to many versions of the development of the projects, the possibility of redundant test cases in test suite is more .The redundant test case may in respect to the testing requirements for which they were generated. Due to limitation of time and resource for retesting the software every time before a new version is released, it is really important to search for techniques that ensure manageable test suites size by removing redundant test cases without hampering the performance of the software. This process is popularly called test suite minimization.

The motivation behind this topic is that the time taken and cost involved in testing phase of software development life cycle increases rapidly as the test suite size increases.So to obtain the correct results i.e. minimized test suite that will reduce the cost and effort involved in testing.

We have used incremental model of software development life cycle as after the Require- ment Gathering and Analysis we have built different modules and each module is being tested before we built the next module to reach at the final results.

As the software is modified and new test cases are added to the test- suite, the size of the test-suite grows and the cost of regression testing increases.This paper investigates the use of an evolutionary approach, called genetic algorithms, for test-suite reduction[2]. The algorithm builds the initial population based on test history, calculates the fitness value using coverage and cost information, and then selectively breeds the successive generations

using genetic operations. This generational process is repeated until a minimized test- suite is found[4].

Test-suite reduction techniques have been extensively studied. Harrold, Gupta, and Soffa proposed a methodology for controlling the size of a test suite. Jones and Harrold presented an algorithm for test-suite reduction that can be tailored effectively for use with Modified Condition/Decision Coverage (MC/DC)[1]. In order to perform test-suite reduction, we should do something includes:

• Maintaining a testing pool where contains all the test cases used in previous test activities. • Keeping the test coverage information which denotes how many and which parts of the program tested by each test cases during the previous tests. • Recording the test-execution cost information that measures the amount of resources each test-cases execution needs.