Test and Evaluation by Genetic Algorithms
IEEE Expert: Intelligent Systems and Their Applications
Evolutionary Testing In Component-based Real-time System Construction
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
An Automated Framework for Structural Test-Data Generation
ASE '98 Proceedings of the 13th IEEE international conference on Automated software engineering
Testing the Results of Static Worst-Case Execution-Time Analysis
RTSS '98 Proceedings of the IEEE Real-Time Systems Symposium
Search-based software test data generation: a survey: Research Articles
Software Testing, Verification & Reliability
Structured programming
Evolutionary testing techniques
SAGA'05 Proceedings of the Third international conference on StochasticAlgorithms: foundations and applications
Automated test data generation using a scatter search approach
Information and Software Technology
Search-based multi-paths test data generation for structure-oriented testing
Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation
Evolutionary testing of autonomous software agents
Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
Evolutionary testing of autonomous software agents
Autonomous Agents and Multi-Agent Systems
Search-based software engineering: Trends, techniques and applications
ACM Computing Surveys (CSUR)
Efficient coverage of parallel and hierarchical stateflow models for test case generation
Software Testing, Verification & Reliability
Evolutionary functional black-box testing in an industrial setting
Software Quality Control
Generating test data for both path coverage and fault detection using genetic algorithms
Frontiers of Computer Science: Selected Publications from Chinese Universities
| Hi-index | 0.01 |
The development and testing of software-based systems is an essential activity for the automotive industry. The 50-70 software-based systems with different complexities and developed by various suppliers are installed in today's premium vehicles, communicating with each other via different bus systems. The integration and testing of systems of this complexity is a very challenging task. The aim of testing is to detect faults in the systems under test and to convey confidence in the correct functioning of the systems if no faults are found during comprehensive testing. Faults not found in the different testing phases could have significant consequences that range from customer dissatisfaction to damage of physical property or, in safety-relevant areas, even to the endangering of human lives. Therefore, the thorough testing of developed systems is essential. Evolutionary testing tries to improve the effectiveness and efficiency of the testing process by transforming testing objectives into search problems, and applying evolutionary computation in order to solve them. The most important class of testing methods is functional testing. However, functional testing is difficult to automate by evolutionary testing. This work will describe how evolutionary testing could be applied to automate functional testing in general and the testing of complex automotive systems in particular. It presents two case studies and shows how evolutionary testing is effective at finding faults in the functional behaviour of these systems. In addition, a quantitative comparison with manual and random test case selection is done for one application.