Machine Learning-based Software Effort Estimation : An Analysis

被引:0
作者
Polkowski, Zdzislaw [1 ]
Vora, Jayneel [2 ]
Tanwar, Sudeep [2 ]
Tyagi, Sudhanshu [3 ]
Singh, Pradeep Kumar [4 ]
Singh, Yashwant [5 ]
机构
[1] Jan Wyzykowski Univ, Polkowice, Poland
[2] Nirma Univ, Inst Technol, Dept Comp Engn, Ahmadabad, Gujarat, India
[3] Deemed Univ, Dept ECE, Thapar Inst Engn & Technol, Patiala, Punjab, India
[4] Jaypee Univ Informat Technol, Dept Comp Sci & Engn, Waknaghat, India
[5] Cent Univ Jammu, Dept Comp Sci & Informat Technol, Jammu, Jammu & Kashmir, India
来源
PROCEEDINGS OF THE 11TH INTERNATIONAL CONFERENCE ON ELECTRONICS, COMPUTERS AND ARTIFICIAL INTELLIGENCE (ECAI-2019) | 2019年
关键词
Effort estimation; software engineering; machine learning; attribute selection; DATA ANALYTICS; INTERNET; TAXONOMY;
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Estimating the effort behind a software project is the approximation time and resources an engineer need to create a software application. The estimation is one of the most important phase in the developing process to set the cost of project and ultimately to attract the client. In the preliminary stage of a project, the accuracy of estimation is to be extremely precise and dependable, which may not be easy to achieve. Therefore, use of machine learning algorithms is a possible solution for the estimation process on which the decision can be made. In this study, we have analyzed various studies and machine learning trends conducted in this field. Doing this effective reductions in the cost and parameter for the project to be accomplished. Accuracy, root mean and relative absolute errors are used to compute the effort estimation accuracy.
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页数:6
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