Enhanced framework for ensemble effort estimation by using recursive‐based classification

Abstract

Abstract Service‐oriented software engineering is a software engineering methodology focussed on the development of software systems. The systematic application of technological and scientific knowledge depends on the methodology, experience, design for obtaining efficient implementation, testing and software documentation. Software effort estimation (SEE) plays an essential role in reusable service for ensembling the effort estimation of the software development. Effort estimation is the most efficient process applied in software engineering for the prediction of effort. SEE methods are utilised to achieve the effort, cost and human resources with the assistance of the dataset. It is hard to predict the cost, effort, size and schedule consistently through SEE and hence it causes damage to software enterprises. To overwhelm these limitations, an enhanced support vector regression algorithm is used that extracts the features and delivers the relevant features. This algorithm is used to standardise for main features and is related to the supervised learning algorithms. From this, the best features are extracted followed by the elimination of weakest features using the enhanced recursive elimination algorithm. From the selected features, an enhanced random forest classification is used to classify the results. The outcomes are executed to offer the best accuracy and thereby providing efficient prediction of effort estimation. Finally, the performance is measured with parameters such as Magnitude of Balanced Relative Error (MBRE), mean absolute residual, mean inverted balanced relative error, mean magnitude of error relative and mean magnitude of relative error. On comparing the existing methodologies, it is concluded that the proposed work offers better efficiency.

Keywords

• feature extraction
• learning (artificial intelligence)
• pattern classification
• regression analysis
• software engineering
• support vector machines