Reducing Technical Debt in Agile Projects

Making a Strategy to Pay Off Technical Debt

Assume a development team working on a new project began by adhering to a particular programming standard. They even put up an automated tool to run the code on a regular basis and report on compliance with these standards. However, the development teams became overburdened and stopped using this program after a sprint or two, and when the project director requested a report after a few months, there were numerous problems and warnings, all of which must now be addressed.

Agile teams who are committed to delivering the most amount of customer value feasible throughout each sprint frequently encounter this situation. Despite having all the functions in place, the issue has to be resolved right now since the team does not want to deploy code that does not meet production standards. The team is then confronted with a few debt-service choices available:

• Divide all defects and warnings among the development team and delegate corrections to them during the following sprint, in addition to their usual development work, by scheduling additional hours.
• Estimate the number of refactoring stories and either schedule them as new user stories for forthcoming sprints or incorporate them into existing user stories.
• Negotiate the number of user stories intended for the following sprint with the product owner in order to have some more time for reworking the code.
• Devote an entire sprint to refactoring code.
• Plan to distribute this effort across several sprints and set a deadline for this assessment before the release's completion.

Though all of these are legitimate options, the ideal strategy is determined by the team, the context, forthcoming deadlines, the level of risk the team is ready to take, the top priority for functionality that must be provided, and engagement with the product owner. Again, just as you would when taking out a financial debt, you should plan to pay off technical debt as soon as possible using the resources you have. It's a good idea to conduct a risk assessment of the scenario and come to an agreement with the team on the best course of action.

Key Takeaway: An Agile team can repay technical debt by spreading fixes across the next sprint, estimating refactoring as new user stories, negotiating sprint scope with the product owner, or devoting a whole sprint to refactoring, and the right option depends on deadlines, risk tolerance, and team agreement.

Technical Debt in Software Testing

Technical debt is not limited to programming. Unsatisfactory testing of user stories, letting regression testing stack up for later sprints, not automating essential tests, not having complete test scenarios written, not cleaning up test environments prior to the next new version, and not developing or testing with all test data combinations on current features are all likely to incur technical debts over time. Occasionally debt is accumulated on purpose for the short term, such as failing to update tests with new test data on the final day of the sprint owing to time pressure, but intending to do it within the first couple of days of the next sprint. It's fine to delay certain technical debt for a short time as long as the team approves.

Sometimes debt may be purposefully incurred for a longer period of time by planning ahead. For example, you can decide to delay any system non-functional testing, such as scale, load, or security tests, until a few sprints have passed and features are stable enough to conduct the tests. Again, deferring certain activities is acceptable as long as the team acknowledges the risk and has a strategy to address it. Testing technical debt can allow us to escape difficult situations when necessary, however, you must still take care to prepare meticulously, keep track of the debt, explain it openly and regularly, and pay it off as quickly as possible. Having a strategy in place to pay off these debts eases your strain over time and ensures that your software keeps up its caliber.

AI coding assistants change how quickly this debt builds up. Teams now merge more generated code per sprint than they can review or test, so the gap between shipped code and covered code widens. The DORA State of AI-assisted Software Development 2025 report describes AI as an amplifier that magnifies an organization's existing strengths and weaknesses, so a team that already defers cleanup collects the same debt faster. Treat generated code the way you treat handwritten code: it needs a review, a test, and a named owner before the story closes.

Key Takeaway: Technical debt builds up in testing through deferred regression runs, missing automation, incomplete test scenarios, and uncleaned test environments, and AI-generated code adds technical debt faster unless every generated change gets a review, a test, and a named owner.

How Do You Measure Technical Debt in an Agile Project?

Measure technical debt with static code analysis that converts each code issue into an estimated remediation time, then compare that total against the cost of the code it sits in. SonarQube reports this as two figures. The SonarQube metric definitions state that technical debt is the sum of the maintainability issue remediation costs, that each remediation cost is the effort in minutes evaluated to fix the issue, and that an eight hour day is assumed when the total is shown in days. The technical debt ratio then divides that total by the cost to develop one line of code multiplied by the number of lines of code, with the cost to develop one line set to 30 minutes by default. The maintainability rating is A only when the ratio is at or below 5%.

If you want a definition that does not depend on one vendor, use ISO/IEC 5055:2021. The CISQ code quality standards page describes it as a standard for measuring the internal structure of a software product on four factors: security, reliability, performance efficiency and maintainability. It works by counting weaknesses that static analysis detects in the source code. That gives two teams on different toolchains a common basis for comparing what they carry.

Two habits keep the number useful inside a sprint. First, report the ratio and the direction it moved rather than the raw minutes, because the raw figure grows with the codebase and will always look worse. Second, be clear about what static analysis cannot see. Missing regression tests, unwritten test scenarios and test environments that were never cleaned up do not appear in any code scan, so the testing debt described above still needs its own backlog items with an owner and an estimate.

Key Takeaway: SonarQube measures technical debt as the summed remediation effort for maintainability issues and rates maintainability A only when the technical debt ratio is 5% or lower, while missing regression tests and uncleaned test environments never show up in any code scan.

How Does AI Generated Code Change Technical Debt in Agile Teams?

AI coding assistants change the rate at which technical debt accumulates, not its nature. Teams merge more generated code per sprint than their review and test capacity absorbs, so the gap between shipped code and covered code widens.

The debt lands in familiar places. Generated code often repeats an existing pattern instead of reusing it, so duplication grows across the codebase. Generated tests pass against the generated implementation without asserting the behavior the user story actually asked for. Neither problem is new to Agile teams. The volume is.

Tooling has started to treat this as its own category. SonarQube Server 2026.1 LTA ships a feature called AI Code Assurance, which lets an administrator label a project as containing AI generated code, apply a qualified quality gate with stricter thresholds to it, and publish an assurance badge once the project passes that gate. The documented limitation matters as much as the feature: the label is switched on by hand in Project settings under AI-generated code. SonarQube does not work out on its own which commits an assistant wrote, so the signal is only as accurate as the team that maintains it.

Three rules keep this manageable inside a sprint. Put generated code through the same code review as handwritten code, with a named owner on the pull request. Read every assertion yourself, because a generated test that mirrors a generated bug still passes. Report the debt trend for the labelled projects at the sprint review, so the team sees the direction of travel before it needs a refactoring sprint to recover.

Key Takeaway: AI assistants speed up how fast technical debt builds rather than creating a new kind of it, and SonarQube Server 2026.1 LTA can apply a stricter quality gate to a project labelled as containing AI generated code, though an administrator has to set that label by hand.

Why Do We Need a Definition of Done?

• It provides a simple checklist, which simplifies the development activities of coding, estimation, design etc.
• It helps increase visibility and transparency.
• It reduces rework costs once a feature or story has been accepted as "done."
• It helps create a culture of collaboration and increases communication.
• It provides clear contact between the team and the customer to limit the risk of misunderstanding and assumptions that can lead to conflicts.
• It allows the organization, especially engineering teams, to understand what is expected of them once they make commitments at the beginning of the sprint.
• It increases the efficiency of the entire process because it provides clear criteria about what needs to be completed to finish an artifact, such as a feature, sprint, or user story.

Key Takeaway: A Definition of Done gives an Agile team a shared checklist that raises visibility, reduces rework after a feature is accepted, and removes the misunderstandings that arise when people assume different meanings for "done".