The chaining approach for software test data generation
ACM Transactions on Software Engineering and Methodology (TOSEM)
Fitness Function Design To Improve Evolutionary Structural Testing
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
Evolutionary testing of classes
ISSTA '04 Proceedings of the 2004 ACM SIGSOFT international symposium on Software testing and analysis
Search-based software test data generation: a survey: Research Articles
Software Testing, Verification & Reliability
Evolutionary unit testing of object-oriented software using strongly-typed genetic programming
Proceedings of the 8th annual conference on Genetic and evolutionary computation
A co-evolving decision tree classification method
Expert Systems with Applications: An International Journal
Prototype induction and attribute selection via evolutionary algorithms
Intelligent Data Analysis
Improving evolutionary class testing in the presence of non-public methods
Proceedings of the twenty-second IEEE/ACM international conference on Automated software engineering
Proceedings of the 3rd international workshop on Automation of software test
Search based software testing of object-oriented containers
Information Sciences: an International Journal
Information and Software Technology
ASE '08 Proceedings of the 2008 23rd IEEE/ACM International Conference on Automated Software Engineering
Nature-inspired techniques for conformance testing of object-oriented software
Applied Soft Computing
Evolutionary testing of object-oriented software
Proceedings of the 2010 ACM Symposium on Applied Computing
Lexicographic multi-objective evolutionary induction of decision trees
International Journal of Bio-Inspired Computation
Factors affecting the use of genetic algorithms in test suite augmentation
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Mutation-driven generation of unit tests and oracles
Proceedings of the 19th international symposium on Software testing and analysis
Directed test suite augmentation: techniques and tradeoffs
Proceedings of the eighteenth ACM SIGSOFT international symposium on Foundations of software engineering
Generating parameterized unit tests
Proceedings of the 2011 International Symposium on Software Testing and Analysis
On parameter tuning in search based software engineering
SSBSE'11 Proceedings of the Third international conference on Search based software engineering
Bytecode testability transformation
SSBSE'11 Proceedings of the Third international conference on Search based software engineering
MML inference of oblique decision trees
AI'04 Proceedings of the 17th Australian joint conference on Advances in Artificial Intelligence
ICAISC'12 Proceedings of the 11th international conference on Artificial Intelligence and Soft Computing - Volume Part II
Predicting software complexity by means of evolutionary testing
Proceedings of the 27th IEEE/ACM International Conference on Automated Software Engineering
International Journal of Applied Metaheuristic Computing
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As the paradigm of object orientation becomes more and more important for modern IT development projects, the demand for an automated test case generation to dynamically test object-oriented software increases. While search-based test case generation strategies, such as evolutionary testing, are well researched for procedural software, relatively little research has been done in the area of evolutionary object-oriented software testing.This paper presents an approach with which to apply evolutionary algorithms for the automatic generation of test cases for the white-box testing of object-oriented software. Test cases for testing object-oriented software include test programs which create and manipulate objects in order to achieve a certain test goal. Strategies for the encoding of test cases to evolvable data structures as well as ideas about how the objective functions could allow for a sophisticated evaluation are proposed. It is expected that the ideas herein can be adapted for other unit testing methods as well.The approach has been implemented by a prototype for empirical validation. In experiments with this prototype, evolutionary testing outperformed random testing. Evolutionary algorithms could be successfully applied for the white-box testing of object-oriented software.