By Claudio Posada
Talk presented by Claudio Posada – M.Sc. in Artificial Intelligence | Agility Enabler & Agile Coach at Pyxis – at Open Tech 2025, held on Saturday, September 6, 2025.
How can artificial intelligence become a strategic ally to reduce technical debt in software projects? This talk explores real-world cases where AI tools were applied to accelerate Java and WildFly migrations, increase automated testing, improve code security, and enhance team productivity.
In the software world, the word debt doesn’t sound foreign. Every team accumulates it at some point. It can be a quick hack to meet a deadline, a postponed refactor, an outdated dependency, or a configuration left “for later.” Over time, these small technical mortgages add up and start charging interest.
The metaphor “there’s no free lunch” fits perfectly here: every technical decision has a cost. You can postpone it, disguise it, or hide it behind a neat commit… but sooner or later, you’ll have to pay it. The good news is that artificial intelligence can help us pay that debt faster, better, and with less pain.
The term technical debt was coined by Ward Cunningham more than 30 years ago, and it’s more relevant than ever. It describes the compromises we make when we prioritize speed over ideal solutions. Debt isn’t inherently bad—sometimes it’s a conscious tradeoff—but when left unmanaged, it affects system evolution, team velocity, and product quality.
Until recently, managing technical debt was almost artisanal: manual code reviews, slow refactors, tedious migrations, and scattered documentation. But today, with AI-based tools, we can tackle complex tasks more strategically and efficiently.
When we talk about AI in software development, we often think of copilots or assistants that write code. But its true potential emerges when we use it to understand and transform existing systems, especially those burdened by years of legacy.
At Pyxis, we’ve experimented with different approaches to make AI a true ally in managing technical debt. It’s not just about generating new code—it’s about helping teams understand, modernize, and improve the code they already have.
And here’s an important caveat: AI doesn’t perform magic. There’s no free lunch. Every improvement requires investment in analysis, training, and validation. But the return can be tremendous.
One of the classic challenges is version migration—for example, moving from Java 8 to 17 or upgrading WildFly. These projects rarely excite teams, take weeks to complete, and almost always break something.
In one real case, a complex system that hadn’t been updated in years achieved a far better outcome thanks to AI-assisted migration. The team retained full responsibility: AI acted as a technical copilot, identifying risks, suggesting changes, and automating repetitive steps—while critical decisions remained in human hands.
Another major source of technical debt is insufficient or nonexistent testing. Many projects inherit codebases without unit tests, and writing them afterward can seem impossible. AI, however, can help generate tests automatically from existing code.
Through static analysis and inference models, it’s possible to detect expected behaviors, propose test cases, and generate basic test suites. In some projects, this increased coverage significantly within days, making future refactoring safer.
Of course, not every AI-generated test is useful. Human review remains essential to validate and adjust relevant cases. But the time saved—and the improved visibility into the system’s real state—is undeniable.
Security is often the most expensive kind of debt. In environments with multiple dependencies, outdated libraries, or custom configurations, risks multiply over time.
AI can analyze entire repositories and detect known vulnerabilities or insecure coding patterns before they reach production.
At Pyxis, we applied tools that combine static code analysis with vulnerability databases (like CVE) and machine learning models that prioritize risks by real impact. This allows teams to focus on fixes that truly matter.
The result: fewer false positives, sharper focus, and a continuous improvement loop where security becomes part of the development flow instead of a reactive afterthought.
In all these cases, AI doesn’t replace developers—it amplifies their capabilities. It’s a tireless assistant that can scan thousands of lines of code, detect inconsistencies, and suggest improvements, while humans focus on design, strategy, and decision-making.
The challenge lies in integrating these tools naturally into the workflow: automated pull requests, pipeline reports, continuous analysis. When AI operates in the same space where development happens, it becomes invisible yet powerful.
From our experience, three lessons repeat across every project where we used AI to reduce technical debt:
Enthusiasm for AI can lead us to use it where it’s unnecessary. Automation for its own sake doesn’t make sense: AI must have a clear purpose. In some cases, a static analysis tool or a good manual refactor can be more effective.
And we must remember that AI introduces its own technical debt—aging models, external dependencies, opaque decisions. Adopting AI brings new ethical and technical responsibilities we must manage.
Reducing technical debt not only improves software quality—it also boosts team morale, system stability, and innovation capacity.
When the codebase is clean, maintainable, and well-documented, ideas flow faster. Properly used, AI accelerates that process. It allows us to pay today what we’ve been postponing for years.
But the warning remains: there’s no free lunch. Every tool requires training, validation, and judgment. AI doesn’t replace experience—but it can make that experience far more productive.
In this new stage of software engineering, artificial intelligence stands as a powerful ally—if we use it with purpose and responsibility.
The invitation is to experiment, measure results, and learn from every attempt. Technical debt won’t disappear overnight, but we can learn to manage it better—with the help of technology and human intelligence.
There’s no free lunch, true. But with a solid strategy and the right tools, we might at least get the coffee .
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