Vol. 4 No. 4 (2025): October
RESEARCH ARTICLES

Risk Prediction in Software Engineering: A Multi-Class Based Approach with FEPP

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  • M Venkata Ramana,
  • S Sudeepthi,
  • K Nitya Sree,
  • S Maksud Hussain,
  • K Naga Sai Ganesh,
  • V Sai Kiran
M Venkata Ramana
Department of Computer Science and Engineering, Annamacharya Institute of Technology and Sciences, Kadapa, Andhra Pradesh, India
S Sudeepthi
Department of Computer Science and Engineering, Annamacharya Institute of Technology and Sciences, Kadapa, Andhra Pradesh, India
K Nitya Sree
Department of Computer Science and Engineering, Annamacharya Institute of Technology and Sciences, Kadapa, Andhra Pradesh, India
S Maksud Hussain
Department of Computer Science and Engineering, Annamacharya Institute of Technology and Sciences, Kadapa, Andhra Pradesh, India
K Naga Sai Ganesh
Department of Computer Science and Engineering, Annamacharya Institute of Technology and Sciences, Kadapa, Andhra Pradesh, India
V Sai Kiran
Department of Computer Science and Engineering, Annamacharya Institute of Technology and Sciences, Kadapa, Andhra Pradesh, India
DOI: https://doi.org/10.5281/zenodo.15250720

Published 2025-04-20

Keywords

  • Software risk prediction,
  • role-based risk analysis,
  • multi-class classification,
  • predictive modeling,
  • machine learning in software engineering

How to Cite

M Venkata Ramana, S Sudeepthi, K Nitya Sree, S Maksud Hussain, K Naga Sai Ganesh, & V Sai Kiran. (2025). Risk Prediction in Software Engineering: A Multi-Class Based Approach with FEPP. International Journal of Computational Learning & Intelligence, 4(4), 672–679. https://doi.org/10.5281/zenodo.15250720

Copyright (c) 2025 M Venkata Ramana, S Sudeepthi, K Nitya Sree, S Maksud Hussain, K Naga Sai Ganesh, V Sai Kiran

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Abstract

Accurate risk prediction plays a crucial role in the successful execution of software projects by identifying potential threats and weaknesses early in the development process. This paper introduces an innovative multi-class, role-specific strategy for predicting risks in software engineering, incorporating Feature Extraction and Prioritization Paradigm (FEPP) to improve the precision and efficiency of risk detection. The model is specifically tailored to handle complex risk patterns associated with various team roles such as developers, testers, and project managers. Through this methodology, the system aims to support proactive risk mitigation, ensuring better project outcomes.

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