Genetic algorithms: a powerful tool for large-scale nonlinear optimization problems
Computers & Geosciences
Performing data flow testing on classes
SIGSOFT '94 Proceedings of the 2nd ACM SIGSOFT symposium on Foundations of software engineering
Automatic Generation of Path Covers Based on the Control Flow Analysis of Computer Programs
IEEE Transactions on Software Engineering
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
Generating Software Test Data by Evolution
IEEE Transactions on Software Engineering
A New Representation And Crossover Operator For Search-based Optimization Of Software Modularization
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
A Multiple Hill Climbing Approach to Software Module Clustering
ICSM '03 Proceedings of the International Conference on Software Maintenance
Optimizing Testing Efficiency with Error-Prone Path Identification and Genetic Algorithms
ASWEC '04 Proceedings of the 2004 Australian Software Engineering Conference
An approach for QoS-aware service composition based on genetic algorithms
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
ICSM '05 Proceedings of the 21st IEEE International Conference on Software Maintenance
The Automatic Generation of Basis Set of Path for Path Testing
ATS '05 Proceedings of the 14th Asian Test Symposium on Asian Test Symposium
On the Automatic Modularization of Software Systems Using the Bunch Tool
IEEE Transactions on Software Engineering
Clustering the heap in multi-threaded applications for improved garbage collection
Proceedings of the 8th annual conference on Genetic and evolutionary computation
The species per path approach to SearchBased test data generation
Proceedings of the 2006 international symposium on Software testing and analysis
Search Algorithms for Regression Test Case Prioritization
IEEE Transactions on Software Engineering
Constructing the Call Graph of a Program
IEEE Transactions on Software Engineering
Using Genetic Algorithms to Aid Test-Data Generation for Data-Flow Coverage
APSEC '07 Proceedings of the 14th Asia-Pacific Software Engineering Conference
A tabu search algorithm for structural software testing
Computers and Operations Research
Heuristics-based infeasible path detection for dynamic test data generation
Information and Software Technology
An efficient method to generate feasible paths for basis path testing
Information Processing Letters
Finding deadlocks in large concurrent java programs using genetic algorithms
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Compiling finite linear CSP into SAT
Constraints
Generating Feasible Transition Paths for Testing from an Extended Finite State Machine (EFSM)
ICST '09 Proceedings of the 2009 International Conference on Software Testing Verification and Validation
ICSTW '10 Proceedings of the 2010 Third International Conference on Software Testing, Verification, and Validation Workshops
Generating Feasible Test Paths from an Executable Model Using a Multi-objective Approach
ICSTW '10 Proceedings of the 2010 Third International Conference on Software Testing, Verification, and Validation Workshops
Proceedings of the International Conference & Workshop on Emerging Trends in Technology
Evolutionary generation of test data for many paths coverage based on grouping
Journal of Systems and Software
A multiobjective module-order model for software quality enhancement
IEEE Transactions on Evolutionary Computation
Search-based software engineering: Trends, techniques and applications
ACM Computing Surveys (CSUR)
Diversity oriented test data generation using metaheuristic search techniques
Information Sciences: an International Journal
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Path testing is the strongest coverage criterion in white box testing. Finding target paths is a key challenge in path testing. Genetic algorithms have been successfully used in many software testing activities such as generating test data, selecting test cases and test cases prioritization. In this paper, we introduce a new genetic algorithm for generating test paths. In this algorithm the length of the chromosome varies from iteration to another according to the change in the length of the path. Based on the proposed algorithm, we present a new technique for automatically generating a set of basis test paths which can be used as testing paths in any path testing method. The proposed technique uses a method to verify the independency of the generated paths to be included in the basis set of paths. In addition, this technique employs a method for checking the feasibility of the generated paths. We introduce new definitions for the key concepts of genetic algorithm such as chromosome representation, crossover, mutation, and fitness function to be compatible with path generation. In addition, we present a case study to show the efficiency of our technique. We conducted a set of experiments to evaluate the effectiveness of the proposed path generation technique. The results showed that the proposed technique causes substantial reduction in path generation effort, and that the proposed GA algorithm is effective in test path generation.