You can view the full text of this article for free using the link below.

Title: AGS: a precise and efficient AI-based hybrid software effort estimation model

Authors: V. Vignaraj Ananth; S. Srinivasan

Addresses: Department of Computer Science and Engineering, Thiagarajar College of Engineering, Madurai, Tamil Nadu 625 015, India ' Department of Computer Science and Engineering, RMD Engineering College, Kavarapettai, Tamil Nadu, India

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.

Keywords: software effort estimation; artificial intelligence; adaptive neuro fuzzy inference system; ANFIS; lines of code; LoC; genetic algorithm; GA; satin bower bird optimiser; SBO; correlation coefficient; CC; kilo lines of code; KLoC; software complexity.

DOI: 10.1504/IJBIDM.2021.111739

International Journal of Business Intelligence and Data Mining, 2021 Vol.18 No.1, pp.1 - 16

Received: 27 Dec 2017
Accepted: 06 Apr 2018

Published online: 06 Nov 2020 *

Full-text access for editors Access for subscribers Free access Comment on this article