An enhanced test case selection approach for model-based testing: an industrial case study
Proceedings of the eighteenth ACM SIGSOFT international symposium on Foundations of software engineering
Reducing the cost of model-based testing through test case diversity
ICTSS'10 Proceedings of the 22nd IFIP WG 6.1 international conference on Testing software and systems
Using ontology to generate test cases for GUI testing
International Journal of Computer Applications in Technology
Achieving scalable model-based testing through test case diversity
ACM Transactions on Software Engineering and Methodology (TOSEM)
Static test case prioritization using topic models
Empirical Software Engineering
Hi-index | 0.00 |
This paper presents a technique to select subsets of the test cases, reducing the time consumed during the evaluation of a new software version and maintaining the ability to detect defects introduced. Our technique is based on a model to classify test case suites by using an ART-2A selforganizing neural network architecture. Each test case is summarized in a feature vector, which contains all the relevant information about the software behavior. The neural network classifies feature vectors into clusters, which are labeled according to software behavior. The source code of a new software version is analyzed to determine the most adequate clusters from which the test case subset will be selected. Experiments compared feature vectors obtained from all-uses code coverage information to a random selection approach. Results confirm the new technique has improved the precision and recall metrics adopted.