Test Data Generation Based on Test Path Discovery Using Intelligent Water Drop

Automatic test data generation is required to generate test cases dynamically for a specific software program. Manual generation of test data is too tedious and a time consuming task. This paper proposes a technique using Intelligent Water Drop (IWD) for automatic generation of test data. Correctly generated test data helps in reducing the effort while testing the software. This paper discusses different algorithms based on IWD to generate test data and path coverage over Control Flow Graph. Test data is generated keeping in mind all of the programming constraints like “if,” “while,” “do while,” etc., available in the program. DOI: 10.4018/jamc.2012040105 International Journal of Applied Metaheuristic Computing, 3(2), 56-74, April-June 2012 57 Copyright © 2012, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited. AI based technique helps in solving the problem by using fast and proper judgments rather than using step by step deduction (Naaz Raza, 2009). From the last decade various techniques like genetic algorithm, ant colony optimization and other metaheuristic techniques have been introduced for generating test path and test data (Korel, 1990; Michael et al., 1997; Michael & McGraw, 1998; McMinn, 2004; Kewen et al., 2009; Srivastava, Baby, & Raghurama, 2009). The main issues of various metaheuristic techniques (McMinn, 2004) are optimal test data generation and complete software coverage. Another new optimization technique, Intelligent Water Drop (IWD) (Shah-Hosseini, 2009), an algorithm which is based on swarm optimization techniques, can be used for generation of test data. IWD algorithm works similar to the natural water drop of a river bed. This paper applies IWD algorithm to generate test data and also discover existing paths in the program. This paper is structured as follows; first, we describe the background work of software testing. Then, we present the basic IWD optimization approach. The next section applies IWD approach to generate test path and sequence generation. We then describe the case study of the suggested approach for test data generation, while the next section discusses the analysis part. Finally we conclude the paper along with future scope of the applied approach.