| 1 | Andreas Windisch
Search-based Testing of Complex Simulink Models Containing Stateflow Diagrams
Proceedings of 1st International Workshop on Search-Based Software Testing (SBST) in conjunction with ICST 2008Lillehammer, Norway, 9-11 April 2008. |
|
| | Abstract: No abstract |
| | @INPROCEEDINGS{Windisch08,
author = {Andreas Windisch},
title = {Search-based Testing of Complex Simulink Models Containing Stateflow Diagrams},
booktitle = {Proceedings of 1st International Workshop on Search-Based Software Testing (SBST) in conjunction with ICST 2008},
year = {2008},
address = {Lillehammer, Norway},
month = {9-11 April},
pages = {251-251}
} |
| 2 | Maria Prutkina and Andreas Windisch
Evolutionary Structural Testing of Software with Pointers
Proceedings of 1st International Workshop on Search-Based Software Testing (SBST) in conjunction with ICST 2008Lillehammer, Norway, 9-11 April 2008. |
|
| | Abstract: Evolutionary structural testing is an approach for the complete automation of the test data generation process which searches test data providing high structural coverage by means of evolutionary algorithms (EA). Pointer usage complicates the test data generation considerably. An approach for the improvement of evolutionary structural testing is proposed in this paper, allowing effective handling of source code with pointers. |
| | @INPROCEEDINGS{PrutkinaW08,
author = {Maria Prutkina and Andreas Windisch},
title = {Evolutionary Structural Testing of Software with Pointers},
booktitle = {Proceedings of 1st International Workshop on Search-Based Software Testing (SBST) in conjunction with ICST 2008},
year = {2008},
address = {Lillehammer, Norway},
month = {9-11 April},
pages = {231-231}
} |
| 3 | Andreas Windisch and Stefan Wappler and Joachim Wegener
Applying Particle Swarm Optimization to Software Testing
Proceedings of the 9th annual Conference on Genetic and Evolutionary Computation (GECCO '07)London, England, 7-11 July 2007. |
|
| | Abstract: Evolutionary structural testing is an approach to automatically generating test cases that achieve high structural code coverage. It typically uses genetic algorithms (GAs) to search for relevant test cases. In recent investigations particle swarm optimization (PSO), an alternative search technique, often outperformed GAs when applied to various problems. This raises the question of how PSO competes with GAs in the context of evolutionary structural testing.In order to contribute to an answer to this question, we performed experiments with 25 small artificial test objects and 13 more complex industrial test objects taken from various development projects. The results show that PSO outperforms GAs for most code elements to be covered in terms of effectiveness and efficiency. |
| | @INPROCEEDINGS{WindischWW07,
author = {Andreas Windisch and Stefan Wappler and Joachim Wegener},
title = {Applying Particle Swarm Optimization to Software Testing},
booktitle = {Proceedings of the 9th annual Conference on Genetic and Evolutionary Computation (GECCO '07)},
year = {2007},
address = {London, England},
month = {7-11 July},
pages = {1121-1128}
} |
