QE Score and Artificial Intelligence (AI): Measuring Quality in the Era of Augmented Development

Artificial intelligence is profoundly transforming the way we design, develop, test, and deploy software. From code generation to production monitoring, including testing, AI is infiltrating every link in the development chain.

In the face of this transformation, the QE Score — an objective indicator of software quality — can play a far more strategic role: acting as a safeguard against automation, a revealer of blind spots, and a tool for maintaining human coherence in an increasingly machine-driven development process.

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🤖 AI: Now Embedded Across the Software Lifecycle

AI is no longer a gimmick or a marginal assistant. It writes code, tests, fixes, suggests, documents, analyzes logs, predicts incidents, adjusts cloud costs, automates deployments, and detects anomalies… Its integration is transversal, continuous, and sometimes invisible.

But this power raises a central question: how can we ensure that automation doesn’t produce harmful side effects? Massive generation of unused code, over-engineering, decisions made without human validation, pipelines becoming opaque… It has become essential to measure, structure, and frame this new reality.

This is where the QE Score can play a key role.

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🔧 Using AI to Strengthen the QE Score: Augmented Quality

1. 🛠 Analyzing and Automatically Fixing Errors

   AI can diagnose pipeline failures, interpret error logs, suggest solutions… and even propose automatic fixes. It’s no longer just a monitoring tool — it’s becoming a resolution engine.

   
   

2. 🎯 Testing Smarter, Not Just More

   Instead of aiming for arbitrary coverage, AI can analyze the functional and technical criticality of code areas to recommend strategic tests. It can even generate relevant test scenarios based on bug history or real-world usage.

   
   

3. 🔍Monitoring Anomalies Without Relying on Fixed Thresholds

   AI can detect abnormal behavior without predefined thresholds — unusual performance drops, instability spikes, or abnormal regression frequency. The QE Score could thus become adaptive and contextual, tailored to each team or product.

   
   

4. 📊 Improving the Quality of the Data Behind the Score

   A score is only as good as the data that feeds it. AI can identify misclassified bugs, inconsistent tickets, questionable metrics...—helping prevent the score from being skewed by poor project hygiene.

   
   

5. 📚 Giving Meaning to Code Through Semantic Analysis

   AI can “read” code with contextual understanding: grasping intent, detecting dangerous patterns, and spotting side effects that classic static analysis might miss. The QE Score could thus incorporate a business-level reading of quality—not just a technical one.

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🛡 But also... the QE Score as a Safeguard Against AI Drift

With tools capable of generating code at high speed or fixing issues without human oversight, there's a real risk of losing control. Code is generated but never reviewed, tests are automatically created but never run, and biases are introduced by unaudited AI models...

The QE Score can act as a counterbalance by shedding light on:

• Code areas with low test coverage despite high levels of automatic generation,

• Opaque practices (massive commits, missing documentation, over-reliance on external models),

• “Gaps” in quality coverage despite seemingly sophisticated tooling.

In other words, the QE Score can help reintroduce transparency, traceability, and human judgment into an increasingly automated ecosystem.

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🔮 And Tomorrow? A Smart, Proactive, and Self-Adaptive QE Score

We can imagine a future where the QE Score:

• Evolves dynamically based on the project context,

• Integrates weak signals that are undetectable today,

• Interacts with teams to recommend concrete actions (reinforce a specific area, revisit an architecture, improve documentation),

• And even detects biases introduced by AI itself.

A living, learning score—human-centered at its core.

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🔚 Conclusion: Guiding AI with Intelligence

Artificial intelligence is transforming software development. It accelerates, amplifies, and automates. But it also adds complexity, makes systems harder to read, and can create an illusion of control.

In this context, the QE Score—if it fully embraces the contributions of AI while maintaining a human foundation—can become a key balancing instrument: between speed and rigor, automation and responsibility, volume and value.

The future of quality won’t just be measured. It will be augmented—and guided.