Abstract
A bug detection tool is an important tool in software engineering development. Many research papers have proposed techniques for detecting software bug, but there are certain semantic bugs that are not easy to detect. In our views, a bug can occur from incorrect logics that when a program is executed with a particular input, the program will behave in unexpected ways. In this paper, we propose a method and tool for software bugs detection by finding such input that causes an unexpected output guided by the fitness function. The method uses a Hierarchical Similarity Measurement Model (HSM) to help create the fitness function to examine a program behavior. Its tool uses Particle Swarm Optimization (PSO) with Search Space Reduction (SSR) to manipulate input by contracting and eliminating unfavorable areas of input search space. The programs under experiment were selected from four different domains such as financial, decision support system, algorithms and machine learning. The experimental result shows a significant percentage of success rate up to 93% in bug detection, compared to an estimated success rate of 28% without SSR.
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