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Researches on Software Defect Prediction Based on Improved PSO and Fuzzy Integral

Author: DongEnMei
Tutor: JinCong
School: Central China Normal University
Course: Applied Computer Technology
Keywords: Software defect prediction Classification Particle Swarm Optimization Fuzzy Integral Rules to simplify
CLC: TP311.53
Type: Master's thesis
Year: 2011
Downloads: 30
Quote: 1
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Abstract


With the extensive application of computers, computer software demand gradually increases, the problem of how to efficiently develop high-quality computer software software concern. The early development of concepts and methods of computer software greatly limits the quality of the development of the computer and the development cycle. Software defect prediction is made in order to improve the efficiency of software development, reduce development costs, while improving the quality of software development in the field of software engineering. The method by high defect research software module, the use of the statistical classification prediction software modules contain defects. Software developers in the development process will predict the results as a reference to concern prone to defective module, thereby improving the quality of software development, reduce the software development cycle. Currently, the software defect prediction technology to become one of the important areas of software engineering and applications, the technology is more and more. Has important guiding significance for the software defects prediction method to improve the quality of software development and the efficiency of software development, Firstly the basic concepts of computer software defect prediction, significance, Research described in detail in the existing defect prediction techniques were summarized on the basis of the analysis and comparison of the strengths and weaknesses of the existing software defect prediction focus on fuzzy integral theory, design defect prediction model based on fuzzy integral mapping function. The space of the model in the high-dimensional data classification software modules classification hyperplane completion of the module predictions. Misclassification punishment mechanism to adjust the minimum misclassification rate difference of a larger problem for the defects in software module and the proportion of non-defective modules using fuzzy integral classification function. Optimal separating hyperplane function parameter optimization based on genetic optimization method. Secondly, a detailed analysis of the relationship between software modules attribute characteristics of software defects, using the improved particle swarm optimization software defects prediction model. The prediction of the model by optimizing the extraction defective modules feature attribute rules. Experimental results show the validity and applicability of the design model. This paper also defines the interaction between individual particles in the process of particle swarm optimization and inertia weights in the iterative process integrated use of fuzzy measure and fuzzy integral characteristics. In the iteration process, the search direction and speed of the particles varies with the changes in the relationship between all particles, the inertia weight change is automatically modifies the proportion of weight through the the original inertia weight integrated iterative process. Experimental results show that the particle swarm optimization algorithm based on fuzzy integral is feasible.

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CLC: > Industrial Technology > Automation technology,computer technology > Computing technology,computer technology > Computer software > Program design,software engineering > Software Engineering > Software Maintenance
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