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Review of Computer Engineering Research

June 2019, Volume 6, 2, pp 64-75

A Review of Machine Learning Models for Software Cost Estimation

Farrukh Arslan

Farrukh Arslan 1


  1. School of Electrical and Computer Engineering, Purdue University, West Lafayette, USA. 1

on Google Scholar
on PubMed

Pages: 64-75

DOI: 10.18488/journal.76.2019.62.64.75

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Article History:

Received: 12 June, 2019
Revised: 15 July, 2019
Accepted: 20 August, 2019
Published: 27 September, 2019


Abstract:

Software cost estimation is a critical task in software projects development. It assists project managers and software engineers to plan and manage their resources. However, developing an accurate cost estimation model for a software project is a challenging process. The aim of such a process is to have a better future sight of the project progress and its phases. Another main objective is to have clear project details and specifications to assist stakeholders in managing the project in terms of human resources, assets, software, data and even in the feasibility study. Accurate estimation results with definitely helps the project manager to do better estimation for the project cost, the time required for various project phases and resources or assets. This paper builds a software cost estimation model using machine learning approach. Different machine learning algorithms are applied to two public datasets to predict the software cost in the early stages. Results show that machine learning methods can be used to predict software cost with a high accuracy rate.
Contribution/ Originality
This study contributes to the existing literature by enhancing the results of thirteen Machine Learning algorithms on two datasets. The evaluation criteria used in this work are R², MAE, RMAE, RAE, and RRSE. The aim of the proposed model is to predict the effort using dataset attributes and compare them with the actual effort in order to measure the error using different criteria.

Keywords:

Machine learning, Cost estimation, Prediction, Weka, Algorithms, Classification, Prediction models.

Reference:

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Statistics:

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Funding:

This study received no specific financial support.

Competing Interests:

The author declares that there are no conflicts of interests regarding the publication of this paper.

Acknowledgement:


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