EMPIRICAL BASED EFFORT ESTIMATION USING MACHINE LEARNING ALGORITHMS
V. Vignaraj Ananth Dr.S. Srinivasan
Estimation the budget is one of the major tasks in project management. There arises a need to more accurately estimate the required schedule and resources for the software projects. The software estimation process includes estimating the size of the software product, effort needed, development of project schedules, and estimating the overall budget of the project. To estimate the budget we need to consider effort, time and environment. Estimating the effort is the tedious process. Effort is represented as a function of size. In this paper, size is represented in term as Fuzzy number. A new model is approached in this paper by machine learning algorithms to estimate the effort required in the software process. The optimization of the effort parameters is achieved using the M5P technique to obtain better prediction accuracy. Furthermore, performance comparisons of the models obtained using the M5P technique with Random Forest technique are presented in order to highlight the performance achieved by each technique.
This article is written in Adobe PDF format ( .pdf file ).To view this article you need to download the file. Please rightclick on the link below and then select "Save
target as" to download the file to your harddrive.