Author: Ahmed Sayed Ghiduk
Edition:
Publisher: LAP LAMBERT Academic Publishing
Binding: Paperback
ISBN: 3659113123
Category: Programming
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Software Test Data Generation Using Genetic Algorithms: A New Search-Based Testing Technique
In this work, we survey the genetic algorithms based test-data generation techniques to find the limitations of these techniques. Programming books Software Test Data Generation Using Genetic Algorithms pdf. To address the limitations of the surveyed techniques, we present a new technique for automatic test-data generation. Our technique applies genetic algorithms to search for test data to satisfy a wide range of control-flow and data-flow coverage criteria. Our technique guides the search using a new multi-objectives fitness function that evaluates the fitness of the generated test data. We use this technique for automatic test-data generation for covering all-nodes and all-uses coverage criteria for structural and object-oriented programs. Download books Software Test Data Generation Using Genetic Algorithms: a New Search-Based Testing Technique pdf via mediafire, 4shared, rapidshare.
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LAP Lambert Academic Publishing 9783659113123 Software Test Data Generation Using Genetic Algorithms Description In this work, we survey the genetic algorithms based test-data generation techniques to find the limitations of these techniques. To address the limitations of the surveyed techniques, we present a new technique for automatic test-data generation. Our technique applies genetic algorithms to search for test data to satisfy a wide range of control-flow and data-flow coverage criteria.
LAP Lambert Academic Publishing 9783659113123 Software Test Data Generation Using Genetic Algorithms Description In this work, we survey the genetic algorithms based test-data generation techniques to find the limitations of these techniques. To address the limitations of the surveyed techniques, we present a new technique for automatic test-data generation. Our technique applies genetic algorithms to search for test data to satisfy a wide range of control-flow and data-flow coverage criteria.
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Natural language, decision making, nonstationary environments, and other dynamic processes and systems represent some of the greatest challenges facing sixth generation computer technologies. Here, for the first time, is a practical software engineering and applications-oriented work that applies five new neural, genetic, and chaotic paradigms--including the much heralded genetic programming--to a full range of dynamic processes and systems for engineers, designers, developers, and students alike. Each of the paradigms listed below is examined, described, tested, and compared using a range of
Natural language, decision making, nonstationary environments, and other dynamic processes and systems represent some of the greatest challenges facing sixth generation computer technologies. Here, for the first time, is a practical software engineering and applications-oriented work that applies five new neural, genetic, and chaotic paradigms--including the much heralded genetic programming--to a full range of dynamic processes and systems for engineers, designers, developers, and students alike. Each of the paradigms listed below is examined, described, tested, and compared using a range of
Download Software Test Data Generation Using Genetic Algorithms
To address the limitations of the surveyed techniques, we present a new technique for automatic test-data generation. Our technique applies genetic algorithms to search for test data to satisfy a wide range of control-flow and data-flow coverage criteria. Our technique guides the search using a new multi-objectives fitness function that evaluates the fitness of the generated test data. We use this technique for automatic test-data generation for covering all-nodes and all-uses coverage criteria for structural and object-oriented programs. We employ this technique for automatically generating test data for covering the spanning sets. We apply the dominance concepts to decrease the number of the test requirements of all-nodes coverage criterion. Then, we use our technique for automatically generating test suite to satisfy the reduced test requirements of the all-nodes coverage criterion and consequently reducing the size of the test suite. We introduce a set of empirical studies to evaluate the proposed technique and the new evaluation functions. Download free Software Test Data Generation Using Genetic Algorithms: A New Search-Based Testing Technique pdf
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