Machine-Learning Based Model for Improving Effort Estimation using Risk
Ramakrishnan N1, Girijamma H. A.2, Balachandran K.3
1Ramakrishnan N.*, Research Scholar and Associate Professor, Institute of Management, CHRIST (Deemed to be University), Bangalore, India.
2Dr. Girijamma H. A., Professor, Department of Computer Science and Engineering, RNS Institute of Technology, Bangalore, India.
3Dr. Balachandran K., Professor and Head, Department of Computer Science and Engineering, CHRIST (Deemed to be University), Bangalore, India.
Manuscript received on January 13, 2020. | Revised Manuscript received on January 27, 2020. | Manuscript published on February 10, 2020. | PP: 1012-1016 | Volume-9 Issue-4, February 2020. | Retrieval Number: C8957019320/2020©BEIESP | DOI: 10.35940/ijitee.C8957.029420
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: In the real world, many software projects have effort, schedule, and cost overrun. Estimation accuracy is vital for a successful software project. This is a challenge since mostly there is a tendency to over-estimate or under-estimate the size and effort needed in a software project. The prediction of required effort is a critical activity, and a greater focus is required by considering additional factors such as project risk. The importance of project risks and their management has been indicated in literature and its consideration in effort estimation is the focus of this research. This research paper has a focus on proposing a model based on machine learning techniques for improving the effort estimation through inclusion of risk score. The proposed solution considered aggregating the capability of various machine learning models for prediction. The methodology involved usage of extreme gradient boosting algorithm. Data for the research included projects from industry standard dataset and organizational projects. The analysis revealed a reduction in the root mean square error values over multiple iterations suggesting an improvement in model performance. This reveals a better estimation due to minimization of the gap between estimated and actual efforts in a project. This, in turn, would enhance the potential for success of the project through an improved estimation process integrating risk score along with other parameters for estimation of software development effort. The feature importance chart also revealed that project risk score is an important attribute to be considered for effort estimation.
Keywords: Estimation, Estimated Effort, Actual Effort, Project Risk, Machine Learning, Predictive Model
Scope of the Article: Machine Learning