In test data generation approaches, structural coverage criteria are popular and considered cost effective mainly because they are relatively easy to implement. However, we cannot establish a direct link between achieving good structural coverage and test goals such as finding errors. On the other hand, specific criteria can explicitly target such goals but they may require a full formal specification of the program, which makes them expensive and hard to use. In this paper, we propose a new approach to describe targeted test criteria using few structural information of the system under test. The new criteria can easily describe specific situations such as errors and can be transformed into a fitness function. A search technique, in this case ACO (Ant Colony Optimization), can then be used to automatically generate adequate test data. A case study is presented to illustrate the applicably and the usefulness of the approach.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.