Abstract
For the initial steps of the software life cycle, it is essential to handle software estimation, because it assists managers bid on projects and allot resources conventionally. In software planning estimation of the effort is one of the most critical responsibilities. It is necessary to have good effort estimation in order to conduct well budget. The accuracy of the effort estimation of software projects is vital for the competitiveness of software companies. For the forecasting of software effort, it is important to select the correct software effort estimation techniques. Inaccurate effort estimation can be risky to an IT industry’s economics and certainty due to poor quality or trait and stakeholder’s disapproval with the software product. This paper presents the most commonly used machine learning techniques such as Multi-Layer Perceptron, linear regression, decision tree, for effort evaluation in the field of software development.
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