A Simplified Spiking Neural Network for Developer Experience Classification Using Software Engineering Metrics

Bharat Babaso Mane; Rathnakar Achary

doi:10.48084/etasr.19085

Authors

Bharat Babaso Mane Department of Computer Science and Engineering, Alliance University, Bengaluru, Karnataka, India
Rathnakar Achary Department of Computer Science and Engineering, Alliance University, Bengaluru, Karnataka, India

Volume: 16 | Issue: 4 | Pages: 37759-37764 | August 2026 | https://doi.org/10.48084/etasr.19085

Received: 3 April 2026 | Revised: 20 April 2026, 1 May 2026, and 25 May 2026 | Accepted: 9 June 2026 | Online: 18 June 2026

Corresponding author: Bharat Babaso Mane

Abstract

In modern enterprises, software development is an important part, as businesses are increasingly relying on it to support and improve operational performance. Nevertheless, low-quality software decreases the level of satisfaction of clients. Software development is now considered a combined and technology-dependent activity, and the developer's experience can play an essential role in shaping the quality of software they generate. In software development, the allocation of the appropriate software developers (for example, those who have appropriate coding skills) to a project is an important feature. The difficulty lies in the fact that it is very complicated for project managers, clients, and software development companies to find a suitable developer to allocate a specific project. Therefore, there is a need for a scalable mechanism to identify the level of coding expertise of the software developer. Deep Learning (DL) methods have been extensively applied to assess the impact of developers' experience on code quality. This study presents an Empirical Evaluation of Developer Experience-Based Software Quality Estimation Using Spiking Neural Networks and Metaheuristic Optimization (EESQA-DELMOA) model, which aims to assess software quality by analyzing the developers' experience levels on code reliability and maintainability. EESQA-DELMOA employs a Bio-inspired Artificial Hummingbird (BAHB) technique to select the most relevant features for improving model performance. Subsequently, a Simplified Spiking Neural Network (SSNN) is deployed for classification. Finally, parameter tuning is performed using an Adaptive Migration Butterfly Optimization Algorithm (AMBOA) to improve the classification performance of the SSNN classifier. EESQA-DELMOA was experimentally evaluated using a benchmark dataset, and the results demonstrate its enhanced performance compared to recent approaches.

Keywords:

software quality assessment, developers experience level, adaptive migration butterfly optimization algorithm, feature selection, deep learning

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