Automatic mutation test input data generation via ant colony

Fault-based testing is often advocated to overcome limitations of other testing approaches; however it is also recognized as being expensive. On the other hand, evolutionary algorithms have been proved suitable for reducing the cost of data generation in the context of coverage based testing. In this paper, we propose a new evolutionary approach based on ant colony optimization for automatic test input data generation in the context of mutation testing to reduce the cost of such a test strategy. In our approach the ant colony optimization algorithm is enhanced by a probability density estimation technique. We compare our proposal with other evolutionary algorithms, e.g., Genetic Algorithm. Our preliminary results on JAVA testbeds show that our approach performed significantly better than other alternatives. Categories and Subject Descriptors D [Software]: Miscellaneous; D.2.5 [Software Engineering]: Testing and Debugging —Testing tools (e.g., data generators, coverage testing) Gener...

• Type: Conference
• Year: 2007
• Where: GECCO
• Authors: Kamel Ayari, Salah Bouktif, Giuliano Antoniol
• Keywords: Ant Colony Optimization, Data Generation, GECCO 2007, Input Data Generation