A Data-Driven Artificial Neural Network Approach to Software Project Risk Assessment
In the realm of software project management, predicting and mitigating risks are pivotal for successful project execution. Traditional risk assessment methods have limitations in handling complex and dynamic software projects. This study presents a novel approach that leverages artificial neural net...
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2023-01-01
|
| Series: | IET Software |
| Online Access: | http://dx.doi.org/10.1049/2023/4324783 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849412414409277440 |
|---|---|
| author | Mohammed Naif Alatawi Saleh Alyahyan Shariq Hussain Abdullah Alshammari Abdullah A. Aldaeej Ibrahim Khalil Alali Hathal Salamah Alwageed |
| author_facet | Mohammed Naif Alatawi Saleh Alyahyan Shariq Hussain Abdullah Alshammari Abdullah A. Aldaeej Ibrahim Khalil Alali Hathal Salamah Alwageed |
| author_sort | Mohammed Naif Alatawi |
| collection | DOAJ |
| description | In the realm of software project management, predicting and mitigating risks are pivotal for successful project execution. Traditional risk assessment methods have limitations in handling complex and dynamic software projects. This study presents a novel approach that leverages artificial neural networks (ANNs) to enhance risk prediction accuracy. We utilize historical project data, encompassing project complexity, financial factors, performance metrics, schedule adherence, and user-related variables, to train the ANN model. Our approach involves optimizing the ANN architecture, with various configurations tested to identify the most effective setup. We compare the performance of mean squared error (MSE) and mean absolute error (MAE) as error functions and find that MAE yields superior results. Furthermore, we demonstrate the effectiveness of our model through comprehensive risk assessment. We predict both the overall project risk and individual risk factors, providing project managers with a valuable tool for risk mitigation. Validation results confirm the robustness of our approach when applied to previously unseen data. The achieved accuracy of 97.12% (or 99.12% with uncertainty consideration) underscores the potential of ANNs in risk management. This research contributes to the software project management field by offering an innovative and highly accurate risk assessment model. It empowers project managers to make informed decisions and proactively address potential risks, ultimately enhancing project success. |
| format | Article |
| id | doaj-art-5a8ebc533af64d61b738b7fc89abf5af |
| institution | Kabale University |
| issn | 1751-8814 |
| language | English |
| publishDate | 2023-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Software |
| spelling | doaj-art-5a8ebc533af64d61b738b7fc89abf5af2025-08-20T03:34:26ZengWileyIET Software1751-88142023-01-01202310.1049/2023/4324783A Data-Driven Artificial Neural Network Approach to Software Project Risk AssessmentMohammed Naif Alatawi0Saleh Alyahyan1Shariq Hussain2Abdullah Alshammari3Abdullah A. Aldaeej4Ibrahim Khalil Alali5Hathal Salamah Alwageed6Information Technology DepartmentApplied College in DwadmiDepartment of Software EngineeringCollege of Computer Science and EngineeringDepartment of Management Information SystemsDepartment of Instructional TechnologyCollege of Computer and Information ScienceIn the realm of software project management, predicting and mitigating risks are pivotal for successful project execution. Traditional risk assessment methods have limitations in handling complex and dynamic software projects. This study presents a novel approach that leverages artificial neural networks (ANNs) to enhance risk prediction accuracy. We utilize historical project data, encompassing project complexity, financial factors, performance metrics, schedule adherence, and user-related variables, to train the ANN model. Our approach involves optimizing the ANN architecture, with various configurations tested to identify the most effective setup. We compare the performance of mean squared error (MSE) and mean absolute error (MAE) as error functions and find that MAE yields superior results. Furthermore, we demonstrate the effectiveness of our model through comprehensive risk assessment. We predict both the overall project risk and individual risk factors, providing project managers with a valuable tool for risk mitigation. Validation results confirm the robustness of our approach when applied to previously unseen data. The achieved accuracy of 97.12% (or 99.12% with uncertainty consideration) underscores the potential of ANNs in risk management. This research contributes to the software project management field by offering an innovative and highly accurate risk assessment model. It empowers project managers to make informed decisions and proactively address potential risks, ultimately enhancing project success.http://dx.doi.org/10.1049/2023/4324783 |
| spellingShingle | Mohammed Naif Alatawi Saleh Alyahyan Shariq Hussain Abdullah Alshammari Abdullah A. Aldaeej Ibrahim Khalil Alali Hathal Salamah Alwageed A Data-Driven Artificial Neural Network Approach to Software Project Risk Assessment IET Software |
| title | A Data-Driven Artificial Neural Network Approach to Software Project Risk Assessment |
| title_full | A Data-Driven Artificial Neural Network Approach to Software Project Risk Assessment |
| title_fullStr | A Data-Driven Artificial Neural Network Approach to Software Project Risk Assessment |
| title_full_unstemmed | A Data-Driven Artificial Neural Network Approach to Software Project Risk Assessment |
| title_short | A Data-Driven Artificial Neural Network Approach to Software Project Risk Assessment |
| title_sort | data driven artificial neural network approach to software project risk assessment |
| url | http://dx.doi.org/10.1049/2023/4324783 |
| work_keys_str_mv | AT mohammednaifalatawi adatadrivenartificialneuralnetworkapproachtosoftwareprojectriskassessment AT salehalyahyan adatadrivenartificialneuralnetworkapproachtosoftwareprojectriskassessment AT shariqhussain adatadrivenartificialneuralnetworkapproachtosoftwareprojectriskassessment AT abdullahalshammari adatadrivenartificialneuralnetworkapproachtosoftwareprojectriskassessment AT abdullahaaldaeej adatadrivenartificialneuralnetworkapproachtosoftwareprojectriskassessment AT ibrahimkhalilalali adatadrivenartificialneuralnetworkapproachtosoftwareprojectriskassessment AT hathalsalamahalwageed adatadrivenartificialneuralnetworkapproachtosoftwareprojectriskassessment AT mohammednaifalatawi datadrivenartificialneuralnetworkapproachtosoftwareprojectriskassessment AT salehalyahyan datadrivenartificialneuralnetworkapproachtosoftwareprojectriskassessment AT shariqhussain datadrivenartificialneuralnetworkapproachtosoftwareprojectriskassessment AT abdullahalshammari datadrivenartificialneuralnetworkapproachtosoftwareprojectriskassessment AT abdullahaaldaeej datadrivenartificialneuralnetworkapproachtosoftwareprojectriskassessment AT ibrahimkhalilalali datadrivenartificialneuralnetworkapproachtosoftwareprojectriskassessment AT hathalsalamahalwageed datadrivenartificialneuralnetworkapproachtosoftwareprojectriskassessment |