The debate around technical debt is nothing new, but it’s more relevant now than ever. With teams constantly under pressure to deliver faster results, the risk of accumulating debt grows exponentially. Technical debt isn’t just about cutting corners in code—it’s about the systemic consequences of those decisions over time. The good news? Artificial Intelligence tools are changing the way we approach code, offering more robust support to identify, prevent, and address technical debt before it spirals out of control. In this article, I’ll discuss how AI in code review can be a solid long-term strategy for managing technical debt.
What is Technical Debt?
The concept of technical debt goes far beyond poorly written code or functions that “work but no one understands.” It can generally be divided into three main categories:
The real issue isn’t the existence of technical debt, but how it’s managed. Poorly managed debt increases maintenance costs, reduces productivity, and eventually impacts product competitiveness.
The Side Effects of Poorly Managed Technical Debt:
- Slower implementation of new features.
- Higher risk of critical failures in production.
- Increased developer turnover due to frustration with difficult-to-maintain code.
- Rising infrastructure costs due to inefficient code.
Where Does AI Fit In?
AI can play a key role in three main areas:
- Identifying technical debt.
- Automating quality standards.
- Suggesting intelligent refactoring.
Let’s break these down.
Identifying Technical Debt
Identifying technical debt is often a reactive process.
Serious problems are typically discovered in the final stages of development, during testing, or—worse—in production. Manual code reviews rely heavily on the attentiveness and experience of developers, who are naturally prone to human error, fatigue, and time constraints.
Moreover, complex problems aren’t always obvious at first glance. Bad patterns can be scattered across different parts of the codebase, and their true severity only becomes apparent when something breaks. This leads to last-minute fixes, costly rework, and unnecessary pressure on the team.
One of AI’s biggest strengths is its ability to analyze massive amounts of code consistently and impartially. Unlike manual code reviews, which depend on human availability and focus, AI can:
- Detect recurring patterns of issues.
- Identify duplicated code.
- Map outdated dependencies.
- Suggest improvements based on best practices.
Imagine a tool that, when reviewing a PR, can automatically identify inefficient loops, unhandled exceptions, and code patterns that could cause future headaches. Tools like Kodus do exactly that, providing detailed analyses with concrete evidence.
This level of automation not only reduces trivial errors but also frees up human reviewers for more strategic analysis.
Automating Quality Standards
Maintaining consistent quality standards is one of the biggest challenges in software development, especially in large or distributed teams. Developers have varying levels of experience, personal preferences, and sometimes interpret team guidelines differently.
This inconsistency results in hard-to-maintain code, with mixed styles and broken patterns. Manual reviews often become slow and unproductive, with reviewers spending too much time correcting minor stylistic issues instead of focusing on bigger, more strategic problems.
AI tools solve this problem by automating the enforcement of quality standards. They can automatically check if code adheres to established rules, flag deviations, and suggest corrections directly in the development environment. Plus, they offer real-time feedback, allowing developers to learn and fix problems before even opening a Pull Request.
By eliminating trivial errors through automation, AI allows human reviewers to focus on evaluating logic, architecture, and strategic code decisions, speeding up the entire review process.
Intelligent Refactoring Suggestions
Refactoring is often seen as a luxury that teams can’t afford to prioritize. Under tight deadlines, many developers choose to overlook structural problems in code as long as it “works for now.” But this behavior creates a snowball effect, where small problems accumulate until refactoring becomes a monumental task.
Another recurring issue is the lack of visibility into where refactoring efforts should be focused. Without concrete data, teams risk wasting time on areas that aren’t genuinely problematic.
Refactoring shouldn’t just happen during annual hackathons—it should be a continuous part of the development process, and AI can play a huge role here.
- Identifying legacy code: AI tools can map critical areas that need refactoring.
- Suggesting concrete improvements: AI can recommend more efficient methods, reduce cyclomatic complexity, and remove redundancies.
- Impact assessment: Some tools can even predict how refactoring will affect other parts of the system.
More than just pointing out problems, AI provides clear pathways for refactoring, enabling teams to act confidently, knowing they’re addressing the right issues.
The Long-Term Impact on Technical Debt
When AI actively participates in code reviews, small flaws are corrected immediately, and the cumulative impact is impressive:
- Easier maintenance: AI ensures that code standards are consistently followed, making long-term maintenance much easier.
- Higher software quality: By catching critical errors before they reach production, AI prevents failures that could negatively impact end users.
- Reduced rework: Identifying problems early in the development cycle prevents them from becoming costly challenges later.
- Sustainable scalability: Well-reviewed, debt-free code facilitates system expansion.
- More productive teams: With less time spent on recurring bugs, developers can focus on more strategic and creative tasks, boosting both productivity and team satisfaction.
Conclusion
Artificial Intelligence isn’t a replacement for human reviewers—it’s a powerful extension of their capabilities. By automating analysis, ensuring consistency, and suggesting continuous improvements, AI transforms code review from a bottleneck into a strategic quality tool.
Technical debt will never completely disappear, but with AI, we can manage it intelligently and sustainably.
