Abstract
A software cost estimation is one of the integral parts of project management in every software development organization, which deals with accounting for all the measurable effort required to develop software. This topic in software engineering has been consistently being investigated for the last decade with the intermittent publication of research papers. After reviewing existing approaches, it is found that still, the problem is an open end. Therefore, this paper introduces a machine learning-based approach where a project manager computes the software cost based on the standard input. In contrast, the project manager has estimated cost is further fed to neural network processors subjected to multiple learning algorithms to perform accurate software cost prediction considering all the practical project management scenario. The comparative study outcome shows extensively better accuracy only in three stages of evaluation in the presence of multiple learning approaches.
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Govinda, S. (2022). Framework for Estimating Software Cost Using Improved Machine Learning Approach. In: Saraswat, M., Sharma, H., Balachandran, K., Kim, J.H., Bansal, J.C. (eds) Congress on Intelligent Systems. Lecture Notes on Data Engineering and Communications Technologies, vol 114. Springer, Singapore. https://doi.org/10.1007/978-981-16-9416-5_53
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DOI: https://doi.org/10.1007/978-981-16-9416-5_53
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