Knowledge Lever Risk Management for Software Engineering: A Stochastic Framework for Mitigating Knowledge Loss
Mark Chua, Samuel Ajila

TL;DR
This paper introduces the KLRM framework for managing knowledge risks in software engineering, using a stochastic model to quantify how activating knowledge levers improves project outcomes and reduces knowledge loss.
Contribution
It proposes a novel structured framework and stochastic model to actively manage intangible knowledge assets in software projects, enhancing risk mitigation strategies.
Findings
Full lever activation increases knowledge capital by 63.8%.
Lever activation virtually eliminates knowledge crisis probability.
Improved project alignment reduces rework and rediscovery costs.
Abstract
Software engineering (SE) organizations operate in a knowledge-intensive domain where critical assets -- architectural expertise, design rationale, and system intuition -- are overwhelmingly tacit and volatile. The departure of key contributors or the decay of undocumented decisions can severely impair project velocity and software quality. While conventional SE risk management optimized for schedule and budget is common, the intangible knowledge risks that determine project success remain under-represented. The goal of this research work is to propose and evaluate the Knowledge Lever Risk Management (KLRM) Framework, designed specifically for the software development lifecycle. The primary objectives are to: (1) recast intangible knowledge assets as active mechanisms for risk mitigation (Knowledge Levers); (2) integrate these levers into a structured four-phase architecture (Audit,…
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