A novel framework for risk management of software projects by integrating a new COPRAS method under cloud model and machine learning algorithms

Project risk management which has been rarely considered, especially in software projects, is a crucial process to complete projects successfully. This paper aims to propose a novel project risk management framework both to evaluate the project risks and to predict the success or failure of software...

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Bibliographic Details
Published in:Annals of operations research Vol. 338; no. 1; pp. 675 - 708
Main Authors: Haghighi, Mohammad Hossein, Ashrafi, Maryam
Format: Journal Article
Language:English
Published: New York Springer US 01.07.2024
Springer
Springer Nature B.V
Subjects:
Algorithms
Business and Management
Combinatorics
Decision trees
Evaluation
Failure
Feature recognition
Machine learning
Operations Research/Decision Theory
Original Research
Pattern recognition
Project management
Risk management
Software
Statistical analysis
Success
Technology application
Theory of Computation
Iran
Complex Proportional Assessment (COPRAS)
Machine Learning (ML)
Cloud model
Risk management
Software projects
ISSN:0254-5330, 1572-9338
Online Access:Get full text
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Summary:Project risk management which has been rarely considered, especially in software projects, is a crucial process to complete projects successfully. This paper aims to propose a novel project risk management framework both to evaluate the project risks and to predict the success or failure of software projects based on their risk. This new framework uses Machine Learning (ML) and Multi-Attribute Decision-Making under a cloud model to effectively manage uncertainty. Based on the proposed framework, in the first stage, the important risks of the software projects are identified by an organized approach. Then, the risks are evaluated based on their probability and impacts on time, cost, and quality. In the second stage, the obtained results of the previous stage are entered into a new COPRAS method under the cloud model to rank the risks. Then, the risks are classified into various groups according to their rank. It helps project managers to gain a profound awareness of their high-priority project risks. In this paper, data on risks for fifty software projects has been collected. All the steps of the second stage are implemented on these projects in order to assess their risks. As a result, a data set whose features are nine types of software project risks and the label of success or failure of the projects is created. To recognize the pattern between risks’ values and the success or failure of the projects, various efficient ML algorithms such as Naive Bayes, Logistic regression, Decision Tree, Bagging, Random Forest, and AdaBoost are applied. This framework can predict the success or failure of software projects based on their risks with good accuracy. The results depict that the Naive Bayes algorithm has the best results compared to others.
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ISSN:0254-5330
1572-9338
DOI:10.1007/s10479-023-05653-3