Parallel distributed processing models for economic systems and games
Computer Science in Economics and Management
Automated test data generation using an iterative relaxation method
SIGSOFT '98/FSE-6 Proceedings of the 6th ACM SIGSOFT international symposium on Foundations of software engineering
Automatic test data generation for path testing using GAs
Information Sciences: an International Journal
Generalized extremal optimization: an attractive alternative for test data generation
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Automatic Generation of Floating-Point Test Data
IEEE Transactions on Software Engineering
A System to Generate Test Data and Symbolically Execute Programs
IEEE Transactions on Software Engineering
On the Automated Generation of Program Test Data
IEEE Transactions on Software Engineering
A relation-based method combining functional and structural testing for test case generation
Journal of Systems and Software
A tabu search algorithm for structural software testing
Computers and Operations Research
Automatic, evolutionary test data generation for dynamic software testing
Journal of Systems and Software
Automated test data generation using a scatter search approach
Information and Software Technology
An evaluation of differential evolution in software test data generation
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Evolutionary generation of test data for many paths coverage based on grouping
Journal of Systems and Software
Grouping target paths for evolutionary generation of test data in parallel
Journal of Systems and Software
Cellular automata based test data generation
ACM SIGSOFT Software Engineering Notes
Neural network based black box testing
ACM SIGSOFT Software Engineering Notes
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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.