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Machine Learning Techniques in Software Effort Estimation Using Cocomo Dataset
| Content Provider | Semantic Scholar |
|---|---|
| Author | Bhatia, Sonam Attri, Varinder Kaur |
| Copyright Year | 2015 |
| Abstract | Sonam Bhatia, , Varinder Kaur Attri (Dept .of CSE, GNDU RC Jalandhar, India) (Dept .of CSE, GNDU RC Jalandhar, India) AbstractOne of the most important tasks in software planning and management is estimation of the effort .Software has played an crucial model in software engineering and development for , complex systems. Reliable estimating the software size, cost, effort and schedule greatest challenge for software developers today. Overestimates and underestimates have direct impact for causing damage to software companies. In this paper we introduce a method based on machine learning technique. Linear regression and Multiperceptron are the most popular machine techniques for software development effort estimation. In this paper linear regression and multiperceptron have been used to predict the early stage effort estimations using the COCOMO dataset. It has been found that multiperceptron is able to predict the early stage efforts more efficiently in comparison to the linear regression models. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://ijrdo.org/International-Journal-of-Research-&-Development-Organisation-pdf/International-Journal-Of-Computer-Science-Engineering/Journal-Of-Computer-Science-Engg-June-15/Cse-13.pdf |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
