Applying Back Propagation Neural Network and Particle Swarm Optimization to Estimate Software Effort by Multiple Factors Software Project Clustering

• Main Authors: Hsin-Yin Huang, 黃信穎
• Other Authors: Jin-Cherng
• Format: Others
• Language: zh-TW
• Published: 2013
• Online Access: http://ndltd.ncl.edu.tw/handle/54594920580075089230

碩士 === 大同大學 === 資訊工程學系(所) === 101 === In the technology industry, the problem often encountered in each project's software development is how to estimate the cost of a software development schedule planning and project the necessary manpower, these often come from previous experience to estimate a project required effort and associated costs, once the estimated false may lead to the loss or failure of a project, so an accurate estimate of the effort of each project is very important. The study will be Back Propagation Network software and Particle Swarm Optimization effort analysis and estimate of the project, and use the Pearson product-moment correlation and one-way ANOVA analysis to select a number of factors, and through a different clustering algorithms to the clustering method ( K-mean clustering algorithm and Ward's method clustering algorithm), the MMRE and prediction level (PRED) to compare project, the study of 63 COCOMO in the history of the project to be tested by experimental results, through the clustering project and multiple factors analysis that was compared to originally with the COCOMO three kinds of classification model is more accurate to estimate software effort.