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 and time. Cellular Automata have been applied to things like designing water distribution systems and studying the patterns of migration. This fascinating technique has been amalgamated with standard test data generation techniques to give rise to a technique which generates better test cases than the existing techniques. The approach has been verified on programs selected in accordance with their Lines of Code and utility. The results obtained have been verified. The proposed work is a part of a larger system being developed, which takes into account both black box and white box testing.
Black Box Testing is used when code of the module is not available. In such situations appropriate priorities can be given to different test cases, so that the quality of software is not compromised, if testing is to be stopped prematurely. This paper proposes a framework, which uses requirement analysis and design specification, to prioritize the test cases. The work would be beneficial to both practitioners and researchers.
N-Puzzle problem is an important problem in mathematics and has implications in Artificial Intelligence especially in gaming. The work presented reviews the previous attempts to solve this problem. A formal definition of the problem has been presented. The reason why it is considered as NP hard problem and why Genetic Algorithms (GAs) is applied has been explained. The work here by presents a GAs based algorithm to solve N-Puzzle problem. The algorithm has been analyzed and it is a sturdy belief that the presented algorithm has complexity better than most of the works studied. The work is a part of larger endeavor to solve all NP Hard problems by GAs.
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 following work presents algorithms that handle almost all the constructs of procedural programming languages. The proposed technique uses cellular automata as its base. The use of Cellular Automata brings a blend of artificial life to the work. The work is a continuation of our earlier attempt to amalgamate Cellular Automata based algorithms to generate test data. The technique has been applied to C programs and is currently being tested on a financial enterprise resource planning system. Since, the solution of most of the problems can be found by observing nature, we must explore artificial nature to accomplish the above task.
The Vertex Cover Problem calls for the selection of a set of vertices(V) in a way that all the edges of the graph, connected to those vertices constitute the set E of the given graph G= (V, E). The problem finds applications in various fields and is therefore, one of the most widely researched topics in NP Complete Problems. The problem is an NP Complete problem this work proposes a Genetic Algorithm based solution to handle the problem. The proposed algorithm has been implemented and tested for various graphs. These instances vary in the number of vertices and connectivity. The results are encouraging. This paper also explores the available techniques in order to put the things in the perspective. The future scope of this work intends to apply Diploid Genetic Algorithms to the problem to incorporate robustness into the proposed algorithm.
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