Designing Fair and Scalable AI-Enhanced Software Engineering Performance Reviews

Authors: Aishwarya Babu

DOI: https://doi.org/10.37082/IJIRMPS.v13.i2.232444

Short DOI: https://doi.org/g9g7v2

Country: USA

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Abstract: Performance evaluations in software engineering often struggle with fairness and consistency, particularly in capturing non-code contributions like mentorship and technical leadership. While AI and code analytics offer objectivity and scalability, their over-reliance can reduce nuance. This paper proposes a hybrid framework that integrates AI and natural language processing (NLP) to map traditionally qualitative contributions—such as mentorship impact and design complexity—into measurable signals. By blending conventional code metrics with inferred collaborative behaviors, we introduce a composite metric system designed to ensure fairness across diverse engineering roles and management styles.

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Paper Id: 232444

Published On: 2025-04-29

Published In: Volume 13, Issue 2, March-April 2025

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