Generating test data using ant Colony Optimization (ACO) algorithm and UML state machine diagram in gray box testing approach

• Published in: 2016 International Seminar on Application for Technology of Information and Communication (ISemantic) pp. 217 - 222
• Main Authors: Arifiani, Siska, Rochimah, Siti
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.08.2016
• Subjects: Algorithm design and analysis; Ant Colony Optimization; Clustering algorithms; Gray Box Testing; Software; Software algorithms; Software Statistical Test; Software testing; UML State Machine Diagram; Unified modeling language
• DOI: 10.1109/ISEMANTIC.2016.7873841

There are many algorithms used in software statistical testing such as: search algorithm, genetic algorithm, clustering algorithm, Particle Swarm Optimization (PSO), ant Colony Optimization (ACO) and so on. Based on research, ACO algorithm has been shown that it is outperforms the existing simulated annealing (search algorithm), genetic algorithm and other algorithm in statistical testing for the quality of generating test data and its stability. This ACO algorithm is also comparable to PSO-based method. This research proposes statistical testing technique on Gray Box testing using ACO algorithm. Test case and data test are generated from UML State Machine Diagram. UML State Machine Diagram can describe the structural of source code from Software Under Test (SUT). And it has better coverage of the SUT structural source code than another UML Diagrams. This research aims to get comparison result between UML State Machine Diagram and source code in generating test case and test data base on ACO statistical testing.