Software Development Life Time Prediction Using Machine Learning Approach and There Comparison

6 Pages Posted: 10 Mar 2020

See all articles by Devanshu Dwivedi

Devanshu Dwivedi

University of Petroleum and Energy Studies (UPES)

Apaar Sharma

University of Petroleum and Energy Studies (UPES)

Amar Shukla

University of Petroleum and Energy Studies (UPES)

Date Written: February 15, 2020

Abstract

Software Engineering Project Management (SEPM) contributes to being a key factor in determining the success of a software project. One of the major tasks assigned to it is to estimate the time that needs to be put in the process of development of the software. In real life, we use a plethora of different prediction machine learning algorithms in order to calculate the time it takes, but we have to keep in mind the accuracy and performance of this algorithm. To get the comparative study of the different machine learning techniques that is simple linear-regression, multiple-regressions, Artificial neural networks and the Gaussian-algorithm will be put into action in an attempt to correctly calculate the time it takes in software production keeping in mind the parameters. The dataset that we are trending to test is the content of past software development. Eventually the results that we obtained are published.

Keywords: machine-learning techniques ,software project management ,ANN, GP, LR, MR

Suggested Citation

Dwivedi, Devanshu and Sharma, Apaar and Shukla, Amar, Software Development Life Time Prediction Using Machine Learning Approach and There Comparison (February 15, 2020). 5th International Conference on Next Generation Computing Technologies (NGCT-2019), Available at SSRN: https://ssrn.com/abstract=3538593 or http://dx.doi.org/10.2139/ssrn.3538593

Devanshu Dwivedi (Contact Author)

University of Petroleum and Energy Studies (UPES) ( email )

Energy Acres
P.O. Bidholi via Premnagar,
Dehradun, IN Uttarakhand 248007
India

Apaar Sharma

University of Petroleum and Energy Studies (UPES) ( email )

Energy Acres
P.O. Bidholi via Premnagar,
Dehradun, IN Uttarakhand 248007
India

Amar Shukla

University of Petroleum and Energy Studies (UPES) ( email )

Energy Acres
P.O. Bidholi via Premnagar,
Dehradun, IN Uttarakhand 248007
India

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