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Title page for etd-0904113-144106


URN etd-0904113-144106 Statistics This thesis had been viewed 1002 times. Download 0 times.
Author Hsin-Yin Huang
Author's Email Address No Public.
Department Computer Science and Enginerring
Year 2012 Semester 2
Degree Master Type of Document Master's Thesis
Language zh-TW.Big5 Chinese Page Count 37
Title Applying Back Propagation Neural Network and Particle Swarm Optimization to Estimate Software Effort by Multiple Factors Software Project Clustering
Keyword
  • MMRE
  • Prediction level
  • Back Propagation Network
  • K-Mean clustering algorithm
  • one-way ANOVA
  • Pearson product-moment correlation coefficient
  • software effort
  • Particle Swarm Optimization
  • Ward’s method clustering algorithm
  • Ward’s method clustering algorithm
  • Particle Swarm Optimization
  • software effort
  • Pearson product-moment correlation coefficient
  • one-way ANOVA
  • K-Mean clustering algorithm
  • Back Propagation Network
  • Prediction level
  • MMRE
  • Abstract 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.
    Advisor Committee
  • Jin-Cherng - advisor
  • Jia-Sheng Tsai - co-chair
  • Yung-Chang Hou - co-chair
  • Files indicate in-campus access at 5 years and off-campus access at 5 years
    Date of Defense 2013-07-29 Date of Submission 2013-09-04


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