Abstract
Model-based testing is a technique for generating test
cases from a test model. Various notations and techniques
have been used to express the test model and generate
test cases from those models. Many use customized
modelling languages and in-depth white-box static
analysis for test generation. This allows for optimizing
generated tests to specific paths in the model. Others
use general-purpose programming languages and
light-weight black-box dynamic analysis. While this
light-weight approach allows for quick prototyping and
easier integration with existing tools and user skills,
optimizing the resulting test suite becomes more
challenging since less information about the possible
paths is available. In this paper, we present and compare
three approaches to such black-box optimization.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 2014 Federated Conference on Computer Science and Information Systems |
| Publisher | IEEE Institute of Electrical and Electronic Engineers |
| Pages | 1591-1598 |
| Volume | 2 |
| ISBN (Print) | 978-83-60810-58-3 |
| DOIs | |
| Publication status | Published - 2014 |
| MoE publication type | A4 Article in a conference publication |
| Event | Federated Conference on Computer Science and Information Systems, FedCSIS - Warsaw, Poland Duration: 7 Sept 2014 → 10 Sept 2014 |
Conference
| Conference | Federated Conference on Computer Science and Information Systems, FedCSIS |
|---|---|
| Abbreviated title | FedCSIS |
| Country/Territory | Poland |
| City | Warsaw |
| Period | 7/09/14 → 10/09/14 |
Keywords
- algorithm design and analysis
- analytical models
- generators
- greedy algorithms
- optimization
- radiation detectors
- testing