---
source: arxiv
url: http://arxiv.org/abs/2604.23257v1
published_at: '2026-04-25T11:49:06'
authors:
- Mark Chua
- Samuel Ajila
topics:
- knowledge-management
- software-engineering
- risk-management
- monte-carlo-simulation
- llm-assisted-development
relevance_score: 0.63
run_id: materialize-outputs
language_code: en
---

# Knowledge Lever Risk Management for Software Engineering: A Stochastic Framework for Mitigating Knowledge Loss

## 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.

## Link
- [http://arxiv.org/abs/2604.23257v1](http://arxiv.org/abs/2604.23257v1)