Article: AGS: a precise and efficient AI-based hybrid software effort estimation model Journal: International Journal of Business Intelligence and Data Mining IJBIDM 2021 Vol 18 No.1 pp.1 16 Abstract: To predict the amount of effort to develop software is a tedious process for software companies. Hence, predicting the software development effort remains a complex issue drawing in extensive research consideration. The success of software development process considerably depends on proper estimation of effort required to develop that software. Effective software effort estimation techniques enable project managers to schedule software life cycle activities properly. The main objective of this paper is to propose a novel approach in which an artificial intelligence AI-based technique, called AGS algorithm, is used to determine the software effort estimation. AGS is hybrid method combining three techniques, namely: adaptive neuro fuzzy inference system ANFIS, genetic algorithm and satin bower bird optimisation SBO algorithm. The performance of the proposed method is assessed using a well standard dataset with real-time benchmark with many attributes. The major metrics used in the performance evaluation are correlation coefficient CC, kilo lines of code KLoC and complexity of the software. The experimental result shows that the prediction accuracy of the proposed model is better than the existing algorithmic models. Inderscience Publishers linking academia, business and industry through research

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