Researches On Software Defect Prediction Based On Improved PSO And Fuzzy Integral

Along with the extensive application of computers, the demand for computer software to grow. How to develop high-quality software becomes the key question that software company pay close attention to. But, the early concepts and methods of software development have limited the development of quality and development cycle of the computer in a large extent. Software defect prediction is the research that to improve software development efficiency, reduce development costs and improve the quality of software development in the field of software engineering. Study of the defect modules of software, predict erroneous software modules by classify and statistical analysis methods. The predict result is used as a reference, software developers will focus quality assurance activities on defect-prone modules and thus improve software quality and shortening development cycle by using resources more efficiently in the development process.Now, software defect predict research is becoming an important field in software engineering and applications. And many researchers have already focused on it. Software defect prediction method has great consultive value and important significance to improving software quality and efficiency of software development. The paper begins with an elaborate of basic conceptions, significance and research status about software defect predict based on the survey of the key predict method, and analyzed the disadvantages of existing prediction methods, then researches the fuzzy integral theory, we put forward a software defect predict model based on the mapping of fuzzy integral. It adopts method with ability of clusters from data which has high dimensions to establish the classification hyper-plane in high dimensional space. And draw into the minimum ratio of misclassification to improve the classification. Simulated genetic algorithm is used to optimal the parameter hyper-plane function; We also have analyzed the relation between software defect and attribute, and then build defect prediction model of improved particle swarm optimization to draw classify rule from sample data. Experiment results show that the adaptability and effectiveness of the proposed model.For the connection with the interactivity of fuzzy measure and fuzzy integral, it is applied to improve the performance of PSO in this paper. In the search process, not only the direction of particle search change with all particles, and the iterations auto-modified is integration all of the inertia in the iteration progress. Thus, a novel particle swarm optimization is proposed based on fuzzy measure and fuzzy integral. The result show it is feasible for improvement optimize progress.