Name
Abstract | ||
|---|---|---|
Model-based testing is a promising software testing technique for the automation of test generation and test execution. One obstacle to its adoption is the difficulty of developing models. Learning techniques provide tools to automatically derive automata-based models. Automation is obtained at the cost of time and unreadability of the models. We propose an abstraction technique to reduce the alphabet and large data sets. Our idea is to extract a priori knowledge about the teacher and use this knowledge to define equivalence classes. The latter are then used to define a new and reduced alphabet. The a priori knowledge can be obtained from informal documentation or requirements. We formally prove soundness of our approach. We demonstrate the practical feasibility of our technique by learning a model of the new biometric passport. Our automatically learned model is of comparable size and complexity of a previous model manually developed in the context of testing a passport implementation. Our model can be learned within one hour and slightly refines the previous model. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1007/978-3-642-16558-0_54 | ISoLA (1) |
Keywords | Field | DocType |
reduced alphabet,new biometric passport,previous model,test execution,test generation,model-based testing,passport implementation,promising software testing technique,abstraction technique,derive automata-based model,a priori knowledge,model based testing,software testing | Data mining,Computer science,A priori and a posteriori,Automation,Artificial intelligence,Soundness,Equivalence class,Distributed computing,Inference,Automaton,Biometrics,Documentation,Machine learning | Conference |
Volume | ISSN | ISBN |
6415 | 0302-9743 | 3-642-16557-5 |
Citations | PageRank | References |
34 | 1.36 | 13 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Fides Aarts | 1 | 223 | 8.78 |
| Julien Schmaltz | 2 | 339 | 26.96 |
| Frits Vaandrager | 3 | 1571 | 105.12 |