Targeted adequacy criteria for search-based test data generation

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.