Source note
Knowledge Lever Risk Management for Software Engineering: A Stochastic Framework for Mitigating Knowledge Loss
Knowledge ManagementSoftware EngineeringRisk ManagementMonte Carlo SimulationLLM Assisted Development
Summary
This paper proposes KLRM, a software engineering risk framework for knowledge loss, and tests it with a stochastic simulation model. The main claim is that treating knowledge-sharing practices as explicit risk controls can raise project knowledge capital and cut failure risk.
Problem
- Software teams lose critical tacit knowledge when key developers leave, documentation drifts, or design decisions stay undocumented.
- Standard software risk management focuses on schedule, scope, and budget, while knowledge loss can still slow delivery, raise rework, and hurt quality.
- This matters because software projects depend on human expertise, design rationale, and operational know-how that source code alone does not preserve.
Approach
- The paper defines Knowledge Lever Risk Management (KLRM): a four-phase process of Audit, Alignment, Activation, and Assurance for finding and reducing knowledge-related risks.
- It models project knowledge as a combined score of human capital, structural capital, and relational capital, with an example weighting of 0.40 H + 0.35 S + 0.25 R.
- Knowledge changes through three mechanisms: steady growth, steady decay, and random shock events such as attrition or dependency failure. Structural knowledge grows from human knowledge through a codification link.
- The framework activates software practices as risk controls, including pair programming, mentorship, ADRs, postmortems, CI/CD checks, dependency monitoring, observability, and LLM-assisted development with human review and validation.
- The evaluation uses Monte Carlo simulation with 5,000 runs over 10 years, comparing baseline, single-lever, and full-activation scenarios on expected knowledge capital, coefficient of variation, Sharpe ratio, and crisis probability.
Results
- Full KLRM reaches expected terminal knowledge capital 87.39 versus 53.35 for the baseline, a +63.8% gain.
- Crisis probability drops from 0.64% in the baseline to 0.00% under Full KLRM. The abstract describes this as virtually eliminating knowledge crisis risk.
- Risk-adjusted stability improves: Sharpe ratio 12.99 for Full KLRM versus 9.73 for baseline, and CV 7.7% versus 10.3%.
- Among single levers, Developer Expertise Only performs best with expected knowledge capital 68.19, CV 8.6%, Sharpe 11.65, and 0.00% crisis probability.
- Other single-lever results are smaller: Organizational Memory Only 59.32 expected capital and 0.02% crisis probability; Process Only 58.30 and 0.10%; Ecosystem Relationships 58.15 and 0.06%.
- The evidence is simulation-based from the authors' stochastic model. The excerpt does not report validation on a real software engineering dataset or production deployment.