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Can I read Application of Machine Learning Methods for Software Effort Prediction on EtoBox?
Application of Machine Learning Methods for Software Effort Prediction by Ruchika Malhotra; Arvinder Kaur; Yogesh Singh is a Computer Science article available to read on EtoBox.
What is Application of Machine Learning Methods for Software Effort Prediction about?
Software effort estimation is an important area in the field of software engineering. If the software development effort is over estimated it may lead to tight time schedules and thus quality and testing of software may be compromised. In contrast, if the software development effort is underestimated it may lead to over allocation of man power and resource. There are many models proposed in the literature for estimating software effort. In this paper, we analyze machine learning methods in order to develop models to predict software development effort we used Maxwell data consisting 63 projects. The results show that linear regression, MSP and M5Rules are effective methods for predicting software development effort.
Who reads Application of Machine Learning Methods for Software Effort Prediction?
It is typically read by researchers, students, and practitioners in Computer Science.
- Author
- Ruchika Malhotra; Arvinder Kaur; Yogesh Singh
- Publisher
- ACM
- Published
- 2010
- Language
- EN
- Field
- Computer Science (Physical Sciences)
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