A Transfer Learning-Driven Framework for Enhanced Software Development Effort Estimation Using Optimized Hybrid Deep Learning Model

Badana Mahesh^1,2,*, Mandava Kranthi Kiran³

¹PhD Scholar, GITAM Deemed to be University, Vishakhapatnam, India

²Assistant Professor, Department of Computer Science and Engineering, ANITs, Vishakhapatnam, India

³Assistant Professor, Department of Computer Science and Engineering, GITAM deemed to be University, Vishakhapatnam, India

Emails: mahesh.cse@anits.edu.in; kmandava@gitam.edu

Abstract

Precise assessment of software development effort (SDE) is essential for efficient project planning and resource distribution. Conventional methods frequently encounter difficulties in generalizing across different project areas because of disparate data attributes. This research presents an innovative approach that combines transfer learning with hybrid deep learning models to tackle these difficulties. The platform utilizes pre-trained Random Forest and LSTM models, enhanced using Jaya optimization, to improve prediction accuracy and adapt effectively to new datasets. Transfer learning is utilized to extract reusable patterns and features from source domains, facilitating effortless adaption to target domains with minimum retraining. Extensive experiments on various benchmark datasets illustrate the proposed framework's enhanced performance regarding accuracy, scalability, and robustness relative to leading techniques. This study emphasizes the capability of transfer learning to transform SDE estimates, providing a scalable and domain-adaptive approach for intricate software projects.

Keywords: Software Development Effort Estimation; Hybrid Methodology; Jaya Optimization; Random Forest-LSTM; Transfer Learning