Pang, Der-Jiun and Shavarebi, Kamran and Ng, Sokchoo (2022) Development of machine learning models for prediction of IT project cost and duration. In: 12th IEEE Symposium on Computer Applications and Industrial Electronics, ISCAIE 2022, 21-22 May 2022, Virtual, Online.
Full text not available from this repository.Abstract
Despite the impact of the COVID-19 pandemic in 2020-21, the digital economy remains solid and sustainable. This trend continues to drive massive demand for Information Technology (IT) projects. Underestimated costs and time are considered one of the most critical IT project risks that directly impact a project's success or failure. Currently, there is a lack of models, tools, and techniques capable of effectively predicting cost and duration. This study aims to find a solution to enhance prediction capability by using a machine learning (ML) model. An experiment was conducted comparing the performance of each ML model utilizing three distinct datasets and fourteen different models against six performance indicators. The results indicated the existence of a highly reliable, effective, consistent, and accurate ML model with a significant degree of augmentation compared to conventional predictive project management tools and techniques. © 2022 IEEE.
| Item Type: | Conference or Workshop Item (Paper) |
|---|---|
| Funders: | UNSPECIFIED |
| Uncontrolled Keywords: | Budget control; Costs; Machine learning; Project management; Budget; Digital economy; Duration; Information technology projects; Machine learning models; Machine-learning; Project cost; Project duration; Time; Tools and techniques; Forecasting |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
| Divisions: | Universiti Malaya |
| Depositing User: | Ms. Juhaida Abd Rahim |
| Date Deposited: | 18 Feb 2025 02:21 |
| Last Modified: | 18 Feb 2025 02:21 |
| URI: | http://eprints.um.edu.my/id/eprint/43599 |
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