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Nandal, Deepak. A systematic literature review of machine learning techniques for software effort estimation models. (). King Mongkut's University of Technology North Bangkok. Central Library. : , 2022.
A systematic literature review of machine learning techniques for software effort estimation models
Name: Brar, Pooja
Organization :
University of Science and Technology. Department of Computer Science and Engineering Guru Jambheshwar
Email :
poojabrar0007@gmail.com
Name: Nandal, Deepak
Organization :
University of Science and Technology. Department of Computer Science and Engineering Guru Jambheshwar
Email :
gju.dpknandal@gmail.com
LCSH:
Software engineering
LCSH:
Machine learning
Abstract:
Estimating software projects is a challenging
but necessary process in software development. Predicting the
effort needed to build software is an essential part of the project
life cycle. This paper examines a variety of machine learning
algorithms for estimating effort. There has been a significant
increase in research on effort estimation with machine learning
approaches during the last two decades, with the objective of
improving estimation accuracy. To forecast effort, the
estimation techniques such as expert judgment, COCOMO,
analogy based, putnam model, and machine learning are used.
The algorithmic models' low accuracy and unreliable
architecture resulted in substantial software project risks. As a
result, it is essential to predict the cost of project on an annual
basis and compare it to alternative methods. However, the effort
prediction using machine learning is still limited because a single
technique cannot be treated as best. This paper's main goal is to
present a review of several machine learning approaches for
predicting effort
King Mongkut's University of Technology North Bangkok. Central Library
Address:
BANGKOK
Email:
library@kmutnb.ac.th
Created:
2022
Modified:
2024-05-23
Issued:
2024-05-23
บทความ/Article
application/pdf
BibliograpyCitation :
In IEEE Computer Society. 2022 Fifth International Conference on Computational Intelligence and Communication Technologies (CCICT 2022) (pp.494-499). Los Alamitos, CA : IEEE Computer Society
eng
©copyrights King Mongkut's University of Technology North Bangkok
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ใช้เวลา
0.021696 วินาที
Brar, Pooja
| Title | Contributor | Type |
|---|---|---|
| A systematic literature review of machine learning techniques for software effort estimation models
มหาวิทยาลัยเทคโนโลยีพระจอมเกล้าพระนครเหนือ Brar, Pooja;Nandal, Deepak | บทความ/Article |
Nandal, Deepak
| Title | Contributor | Type |
|---|---|---|
| A systematic literature review of machine learning techniques for software effort estimation models
มหาวิทยาลัยเทคโนโลยีพระจอมเกล้าพระนครเหนือ Brar, Pooja;Nandal, Deepak | บทความ/Article | |
| A novel approach for software effort estimation using optimized C&K metrics
มหาวิทยาลัยเทคโนโลยีพระจอมเกล้าพระนครเหนือ Jangra, Rekha;Sangwan, Om Parkash;Nandal, Deepak | บทความ/Article | |
| A holistic review on optimization strategies for c & k metrics in object-oriented testing
มหาวิทยาลัยเทคโนโลยีพระจอมเกล้าพระนครเหนือ Jangra, Rekha;Sangwan, Om Parkash;Nandal, Deepak | บทความ/Article |