首頁 > 網路資源 > 大同大學數位論文系統

Title page for etd-0902110-114536


URN etd-0902110-114536 Statistics This thesis had been viewed 1515 times. Download 0 times.
Author Sheng-yu Huang
Author's Email Address No Public.
Department Computer Science and Enginerring
Year 2009 Semester 2
Degree Master Type of Document Master's Thesis
Language zh-TW.Big5 Chinese Page Count 38
Title Research on Software Effort Estimation Combined with Genetic Algorithm and Support Vector Regression
Keyword
  • support vector regression
  • COCOMO
  • software effort
  • support vector machine
  • genetic algorithm
  • genetic algorithm
  • support vector machine
  • software effort
  • COCOMO
  • support vector regression
  • Abstract For software developers, accurately forecasting software effort is very important.In the field of software engineering, it is also a very challenging topic. Miscalculated software effort in the early phase might cause a serious consequence. It not only effects the schedule, but also increases the cost price. It might cause a huge deficit. Because all of the different software development team has it is own way to calculate the software effort, the factors affecting project development are also varies. In order to solve these problems, this paper proposes a model which combines genetic algorithm (GA) with support vector machines (SVM). We can find the best parameter of SVM regression by the proposed model, and make more accurate prediction. During the research, we test and verify our model by using the historical datas in four databases, including COCOMO、Desharnais、Kemerer and Albrecht. We will show the results by prediction level (PRED) and mean magnitude of relative error (MMRE).
    Advisor Committee
  • Jin-cherng Lin - advisor
  • Ching-long Yeh - co-chair
  • Jan-min Chen - co-chair
  • Files indicate not accessible
    Date of Defense 2010-07-09 Date of Submission 2010-09-02


    Browse | Search All Available ETDs