This paper presents a quantitative analysis of the transformation of Quality Assurance (QA) driven by Large Language Models (LLMs). Using technology diffusion models, skills-based transition matrices, and an analysis of the compounding performance of transformer-based models, we forecast three distinct scenarios for the QA workforce over the next 3-5 years.
Our analysis indicates that current LLM capabilities are automating 40-60% of foundational QA tasks, with this figure projected to reach 70-85% by 2028 based on current performance curves.
We identify a critical "Adaptation Gap" emerging between the pace of technological change (doubling in capability every 5-8 months) and the pace of workforce reskilling. The paper concludes that the QA profession is undergoing a forced evolution into specialized roles centered on AI validation, quality orchestration, and risk assessment.
The integration of Large Language Models (LLMs) into software development represents a phase change, not a linear progression. The core differentiator of this disruption is its acceleration rate.
Analysis of model performance on benchmarks like HumanEval (code generation) and MMLU (massive multitask language understanding) shows a doubling of capabilities every 5-8 months, far outpacing Moore's Law.
We posit that we are in the early exponential phase of a technology adoption curve that will reshape the profession within 36 months.
We employed a multi-model approach:
We decomposed QA work into 15 core tasks. A panel of 10 experts rated the automativity of each task by current LLMs (2025) and projected for 2028, based on performance trendlines.
We applied this model to forecast the adoption of LLM-powered testing tools. Parameters were calibrated using adoption data from similar disruptive developer tools:
We developed three scenarios and ran 10,000 Monte Carlo simulations for each, varying key parameters like adoption rate, regulatory intervention, and reskilling effectiveness.
Current Average Automation (2025): 53.7%
Projected Average Automation (2028): 78.1%
Growth Rate: 45.4% increase over 3 years
Highest Automation Growth Tasks:
Tasks Remaining Human-Centric:
Using our models, we forecast the following scenarios for the US QA workforce (core ~600,000 professionals) by 2028:
| Scenario | Probability | Net Workforce Change | Traditional Roles | New Roles |
|---|---|---|---|---|
| A: Baseline Transformation | 55% | -4.8% ± 3.2% | -20% | +15% |
| B: Accelerated Displacement | 30% | -24.1% ± 6.5% | -35% | +10% |
| C: Proliferation & Specialization | 15% | +14.3% ± 7.1% | -15% | +30% |
Composite forecast: Most likely outcome of a 5-15% net reduction in total headcount but a ~40% turnover in the skills and roles within the QA domain by 2028.
The Bass Diffusion model indicates that LLM tool adoption will reach 60% of its potential market penetration within 24 months.
However, industry reskilling cycles for a transformation of this magnitude historically take 3-5 years.
Peak Gap: ~30% at Month 31
24-Month Gap: Technology at 71.5%, Workforce at 43.8% (27.7% gap)
This gap represents the period of maximum workforce disruption and opportunity.
The statistical analysis points not to the end of QA, but to its fragmentation and specialization.
| Role | 2025 Share | 2028 Share | Change | Avg Salary 2028 |
|---|---|---|---|---|
| AI Quality Validator | 5% | 25% | +20% | $135K |
| Quality Orchestrator | 15% | 30% | +15% | $148K |
| Continuous Quality Engineer | 10% | 20% | +10% | $125K |
| Traditional & Manual QA | 70% | 25% | -45% | $85K |
For Individuals:
For Organizations:
The data reveals a profession at an inflection point. The compounding improvement of LLMs is not a theoretical future risk but a present-day force deconstructing the foundational tasks of QA.
Our statistical modeling forecasts a most probable future of:
The response must be as dynamic as the technology itself. Success will be determined by the rate at which the human element of QA can ascend the value chain:
From: Validating code
To: Orchestrating intelligent systems and assuring the quality of AI collaborators themselves
The era of I, QA is not one of replacement, but one of forced and necessary evolution.
The QA professional of 2028 will be:
But there will be fewer of them in traditional roles.
Complete analysis and code: https://elamcb.github.io/research/
This research provides a quantitative foundation for understanding and navigating the LLM-driven transformation of software quality assurance.
© 2025 Elena Mereanu - AI-First Quality Engineer