Machine Learning Methods of Effort Estimation and It’s Performance Evaluation Criteria
作者: Rekha Tripathi Dr. P. K. Rai
刊名: International Journal of Computer Science and Mobile Computing- IJCSMC, 2017, Vol.6 (1)
来源数据库: International Journal of Computer Science and Mobile Computing
关键词: Artificial intelligence (AI)Machine learning (ML)Genetic algorithm (GA)Case based reasoning (CBR)Artificial Neural Network (ANN)
原始语种摘要: Effort estimation is important for the control, quality and success of any software development product. Most efficient categories of effort estimation is Expert judgment, Algorithmic estimation and Machine Learning. The aim of this paper is to present the comparative analysis between traditional techniques and Machine Learning (ML) techniques. Results show that ML methods give more accurate effort estimation as compared to the traditional methods of effort estimation. The comparisons of different Machine learning techniques are done in this paper to study that which ML method is more suitable in which situation.
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  • estimation 估计
  • traditional 传统
  • paper 
  • situation 立场
  • effort 努力
  • software 软件
  • efficient 有用的
  • suitable 合适的
  • accurate 精确的
  • development 开发