Enhancing effort estimation in global software development using a unique combination of Neuro Fuzzy Logic and Deep Learning Neural Networks (NFDLNN)

ABSTRACT

Effective project planning and management in the global software development landscape relies on addressing major issues like cost estimation and effort allocation. Timely estimation of software development is a critical focus in software engineering research. With the industry increasingly relying on diverse teams worldwide, accurate estimation becomes vital. Software size serves as a common measure for costs and schedules, but advanced estimation methods consider various variables, such as project purpose, personnel expertise, time and efficiency constraints, and technology requirements. Estimating software costs involve significant financial and strategic commitments, making it crucial to address complexity and versatility related to cost drivers. To achieve enhanced accuracy and convergence, we employ the cuckoo algorithm in our proposed NFDLNN (Neuro Fuzzy Logic and Deep Learning Neural Networks) model. Through extensive validation with industrial project data, using Function Point Analysis as the algorithmic models, our NFA model demonstrates high accuracy in software cost approximation, outperforming existing methods insights of MRE of 3.33, BRE of 0.13, and PI of 74.48. Our research contributes to improved project planning and decision-making processes in global software development endeavours.

KEYWORDS:

• Global Software Development (GSD)
• Lines of Code (LOC)
• Software Project Management (SPM)
• Neuro-Fuzzy Algorithmic (NFA)
• Artificial Neural Network (ANN)
• Artificial Neural Network and Dragonfly Algorithm (ANN-DA)
• Cascade Neural Network (CNN)
• McCabe Complexity (MC)

Disclosure statement

No potential conflict of interest was reported by the author(s).

Authorship contributions

All authors are contributed equally to this work.

Data availability statement

Data sharing not applicable to this article as no datasets were generated or analysed during the current study.

Additional information

Funding

The author(s) reported that there is no funding associated with the work featured in this article.