Implementation of robust software metrics and measurement systems in banking IT management

Posted: Sep 20, 2023

• Luna Garcia
• Luna Jones
• Luna Rodriguez

Abstract

The banking sector represents one of the most complex and regulated software environments, where traditional software metrics frameworks often prove inadequate due to the intricate interplay between technical quality, regulatory compliance, and financial risk management. Conventional software measurement approaches, while valuable in general software engineering contexts, fail to capture the unique characteristics of banking IT systems, including their distributed architecture, stringent security requirements, and the critical relationship between software quality and financial stability. This research addresses this gap by proposing a novel framework that re-conceptualizes software measurement in banking IT as a multi-dimensional optimization problem rather than a simple quality assessment exercise. This paper introduces a quantum-inspired measurement framework that treats software metrics as probabilistic distributions rather than deterministic values, acknowledging the inherent uncertainty in measuring complex banking systems. The approach draws inspiration from federated learning principles to enable privacy-preserving benchmarking across financial institutions, addressing the critical concern of data sovereignty in the highly competitive banking sector. By integrating technical debt assessment with financial risk modeling, the framework provides a more comprehensive understanding of software quality that aligns with business objectives and regulatory requirements.