Prathan, Sorada and Ow, Siew Hock (2020) Determining the best-fit programmers using Bayes' theorem and artificial neural network. IET Software, 14 (4). pp. 433-442. ISSN 17518806, DOI https://doi.org/10.1049/iet-sen.2018.5440.
Full text not available from this repository.Abstract
A data mining-based technique is proposed for the selection and employment of the best-fit programmers to meet the needs of software companies. The proposed technique incorporates Bayes' theorem and artificial neural network (ANN). The datasets used were from two software companies (Company 1 and Company 2) in India, covering the years 2010-2015. Bayes' theorem is used for identifying the prognostic attributes of the best-fit programmers, while the ANN classifier is used for predicting the best-fit programmers. Using a confusion matrix, the ANN classifier performance is 97.2 and 87.3%, 95.8 and 54.5%, and 100 and 75% with regard to accuracy, precision, and recall on the two test datasets of Company 1 and Company 2, respectively. The results show that the technique is effective for predicting the best-fit programmers. Software companies can use this technique in their recruitment and selection process to determine the best-fit employees for the programmer posts. The proposed technique can also be adapted for application in other disciplines such as sports, education, etc., to identify the most suitable person to fill a relevant position.
| Item Type: | Article |
|---|---|
| Funders: | Universiti Malaya (Grant No. PO032-2015A) |
| Uncontrolled Keywords: | neural nets; sport; data mining; recruitment; best-fit programmers; 1 Company 2; software companies; programmer posts; Bayes' theorem; artificial neural network; software development industry; quality software systems; computer programmers; data mining-based technique |
| Subjects: | T Technology > T Technology (General) |
| Divisions: | Faculty of Computer Science & Information Technology > Department of Software Engineering |
| Depositing User: | Ms Zaharah Ramly |
| Date Deposited: | 30 Dec 2023 16:42 |
| Last Modified: | 30 Dec 2023 16:42 |
| URI: | http://eprints.um.edu.my/id/eprint/36524 |
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