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Manual Test Data Generation is an expensive, error prone and tedious task. Therefore, there is an immediate need to make the automation of this process as efficient and effective as possible. The work presented intends to automate the process of Test Data Generation with a goal of attaining maximum coverage. A Cellular Automata system is discrete in space(More)
— An idea of the values between which the roots of the algebraic equation lie is needed in both bisection method and other numerical methods for finding out the roots. Therefore, a method to suggest them is required. Finding them is a search process, so Genetic Algorithms (GA) can be used for the above task as GA's are theoretically and empirically proven(More)
Test Data Generation is the soul of automated testing. The dream of having efficient and robust automated testing software can be fulfilled only if the task of designing a robust automated test data generator can be accomplished. In the work we explore the gaps in the existing techniques and intend to fill these gaps by proposing new algorithms. The(More)
The problem of finding a minimum vertex cover is an NP hard optimization problem. Some approximation algorithms for the problem have been proposed but most of them are neither optimal nor complete. The work proposes the use of the theory of natural selection via Genetic Algorithms (GAs) for solving the problem. The proposed work has been tested for some(More)