Software Effort Estimation using Machine Learning Techniques

被引:0
作者
Monika [1 ]
Sangwan, Om Prakash [1 ]
机构
[1] Guru Jambheshwar Univ Sci & Technol, Dept Comp Sci & Engn, Hisar, Haryana, India
来源
PROCEEDINGS OF THE 7TH INTERNATIONAL CONFERENCE ON CLOUD COMPUTING, DATA SCIENCE AND ENGINEERING (CONFLUENCE 2017) | 2017年
关键词
Effort estimation; literature review; comparative analysis; machine learning; MMRE; ARTIFICIAL NEURAL-NETWORK; MODEL;
D O I
暂无
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Effort Estimation is a very important activity for planning and scheduling of software project life cycle in order to deliver the product on time and within budget. Machine learning techniques are proving very useful to accurately predict software effort values. This paper presents a review of various machine-learning techniques using in estimation of software project effort namely Artificial Neural Network, Fuzzy logic, Analogy estimation etc. Machine learning techniques consistently predicting accurate results because of its learning natures form previously completed projects. This paper summarizes that each technique has its own features and behave differently according to environment so no technique can be preferred over each other.
引用
收藏
页码:92 / 98
页数:7
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