Like many prototypes built under time pressure, the solution delivered value quickly but accumulated technical debt just as fast.
SonarCloud was already integrated and had proven effective in catching obvious issues early. However, it primarily addressed code hygiene. The real shift happened when CodeScene was introduced. CodeScene does not treat code as a static artifact. It analyzes how code evolves, how change frequency, complexity, and ownership interact, and where structural risk accumulates.
Initially, the project was developed solo. Later, a Test Analyst joined the effort. Although not acting as a second developer, the Test Analyst could operate parts of a Technical Test Analyst role. CodeScene metrics, especially Hotspots, Code Health, cohesion, and change frequency, made technical risk visible in a way that was immediately actionable for testing.
From a risk-based testing perspective, the likelihood of functional defects is higher where technical test coverage is low, cohesion is weak, and code smells accumulate. Because the project’s components were well named along domain boundaries, CodeScene enabled a clean transition from technical analysis to domain-focused functional test analysis. This allowed functional testing to start where business risk and technical risk overlapped most.
At the same time, technical debt within the system was not limited to components that directly represented business functionality or appeared in functional specifications. CodeScene made it visible that infrastructural and cross-cutting components (such as shared utilities, integration adapters, configuration logic, and internal frameworks) carried significant structural and evolutionary risk, despite not being considered from a functional perspective. These components often had no clear business ownership, yet they formed the technical backbone of multiple domain features.
From a test analysis standpoint, this required an explicit expansion of scope. To assess risk realistically, it was necessary to maintain visibility over the entire technical landscape rather than focusing solely on business-facing components. By incorporating CodeScene’s Hotspot analysis and Code Health indicators, functional and technical test analysis could be aligned around both domain impact and systemic risk. This holistic view ensured that hidden technical debt in background components was considered in test prioritization, reducing the likelihood that structural weaknesses would later surface as critical functional failures.
The Hotspot view became one of the strongest accelerators. Instead of spreading refactoring and testing effort evenly, it revealed where high complexity and high change frequency intersected. These areas not only benefit most from refactoring but also deserve deeper functional validation. This pattern is not project-specific; in most evolving systems, a small subset of the codebase carries a disproportionate amount of change risk.
One of the most valuable outcomes was the creation of a shared language between development and testing. Technical risk signals became input for functional discussions instead of remaining isolated within engineering. This alignment reduced friction and improved prioritization across roles.
SonarQube and CodeScene serve different but complementary purposes. SonarQube enforces static rules and hygiene. CodeScene explains where change truly matters by revealing historical behavior, architectural drift, and socio-technical risk. Together, they provide both discipline and direction.
The project evolved into a modern DevOps workflow integrating automated accessibility tests, unit tests, API tests, and cross-browser GUI regression tests. Linters ran directly in the IDE, CodeScene feedback was available during development, and AI tools supported documentation, reasoning, and refactoring. AI was used deliberately as augmentation, not replacement for engineering judgment.
Metrics do not replace expertise. Used blindly, they can create false confidence. Used consciously, they amplify judgment. CodeScene highlights risk, but domain knowledge and exploratory thinking remain essential.
• Technical metrics can guide functional test prioritization
• Risk-based testing benefits from structural code insights
• A small part of the system usually carries most change risk
• Shared risk language improves collaboration across roles
• Tools enable better decisions, but do not replace accountability
To reduce technical debt faster by focusing on structural risk instead of surface-level symptoms.
By combining static analysis, behavioral code analysis, and risk-based testing into a coherent quality strategy.
A pragmatic, transferable approach that aligns development, testing, and delivery around where change truly matters.