This article is based on reporting originally published by TechTarget, incorporating insights from software engineers and consultants interviewed at DeveloperWeek 2017.
What Is Technical Debt and Why Does It Matter?
Technical debt—sometimes called code debt—is what accumulates when low-quality or defective code ships, or when discovered defects are deferred rather than resolved. It is a common byproduct of iterative development where speed of release is prioritized over code quality.
The metaphor is deliberate: just as financial debt accrues interest, unresolved code defects become increasingly expensive to fix over time. Mik Kersten, CEO of Tasktop Technologies, put it plainly: “You have to pay down that technical debt. Otherwise, a part of the application portfolio is a dead end, and it’s going to sink the company with overtime costs needed to do maintenance.”
The following five strategies—drawn from practitioners across the software industry—offer a concrete path to reducing technical debt without sacrificing delivery speed.
1. Define DevOps Best Practices and Actually Enforce Them
Automation of tools gets the headlines, but automating the entire application lifecycle management process is equally important. Michael Irigoyen, software engineer at Accusoft, made this point at DeveloperWeek 2017: “Automate the end-to-end process.”
Matt Heusser, principal consultant at Excelon Development, observed that too many organizations buy tools and drop them into the pipeline without first establishing foundational processes. When software quality suffers, they blame the tool—but the root cause is the absence of defined practices.
Christopher Tozzi, analyst at Fixate IO, reinforced this: “Doing things the quick-and-dirty way [can create] quick-and-dirty code snippets, which can add up to big technical debt across a large program.” Enforcing best practices stops developers from taking process shortcuts that introduce defects upstream.
Heusser added that improving first-time quality requires genuine collaboration between development and test. That means building a component architecture, improving communication, and using practices like the “three amigos” (developer, tester, and business analyst reviewing requirements together) to surface ambiguities before code is written.
At Accusoft, Irigoyen described how regularly scheduled two-day hackathons give developers dedicated time to solve process problems. These sessions have produced practical tools including a log overview and analyzer, a programmed drone that reacts to build failures, and a CSS style reference site for UI developers.
2. Automate All Non-Value Work
Not all work moves the product forward. Compliance reporting, activity documentation, and manual handoffs between team members are necessary but add no direct value to the customer. Kersten calls these “non-value activities,” and argues they must be automated aggressively.
The problem compounds in Agile environments. “When you go from nine-month release cycles to four-week iterations, your people have to do the same non-value work that they would normally do every nine months in four weeks,” Kersten explained. Automation of these activities is critical to sustaining DevOps velocity without driving up technical debt.
Practical examples of non-value work ripe for automation include:
- Compliance evidence collection and reporting
- Manual status updates across tools and stakeholders
- Work item handoffs between teams or stages in the pipeline
- Environment provisioning and configuration management
3. Fix Defects Early—Don’t Let Flaws Fester
The cost of fixing a software defect rises dramatically the longer it remains unresolved. Theresa Lanowitz, founder of voke inc., frames it clearly: “The later in software development that a defect is identified or found, the more expensive it is to resolve or remediate the issue.”
Historical research by Barry Boehm and Victor Basili found that fixing bugs in production was approximately 1,000 times more expensive than catching them during the requirements phase. A 2008 IBM Systems Sciences Institute report estimated that defects discovered after release cost 30 times more to fix than those caught before release.
Beyond direct cost, Heusser highlighted the operational drag of a growing defect backlog: “When a new bug is reported, people have to check the defect tracker to see if it was identified but marked ‘fix-later.’ The open defect list begins to look big.” This creates overhead in every sprint and erodes team morale.
Tozzi added a customer-facing dimension: bugs that reach production require public-facing critical updates, user notifications, and patching cycles—not all users will install updates promptly, leaving a portion of the user base exposed. Each production incident carries reputational risk that compounds the financial cost.
4. Don’t Rush the Requirements Phase
A persistent misconception in Agile development is that working in smaller increments makes requirements easier to get right. Lanowitz challenged this directly: “People think that developing stuff in smaller pieces now means there are fewer and more obvious requirements.” In practice, the opposite can be true—users receive features faster and form opinions faster, raising the stakes for getting requirements correct from the start.
Poor requirements elicitation remains a primary driver of rework, regardless of development methodology. Agile did not eliminate the need for careful upfront thinking about what a feature should do—it redistributed when that thinking happens, but did not reduce its importance.
Teams can mitigate requirements-driven rework by:
- Using behavior-driven development (BDD) practices to write acceptance criteria before coding begins
- Running structured “three amigos” sessions for each user story
- Validating requirements with actual users or product owners before sprint commitment
- Treating automated acceptance tests as living documentation of requirements
5. Prioritize Test Automation to Reduce Technical Debt
All the practitioners quoted in the source reporting agreed: without automated testing, it is effectively impossible to manage technical debt in Agile and DevOps environments at scale.
Lanowitz summarized the benefit concisely: “Automate all the traditional types of tests. It eliminates the overall total number of defects, reduces your test cycle and reduces your overall software cycle.”
Automated testing acts as a safety net that catches regressions before they accumulate into debt. Key areas to automate include:
- Unit tests: Validate individual components in isolation at commit time
- Integration tests: Confirm that components work correctly together
- Regression tests: Prevent previously fixed bugs from reappearing
- Acceptance tests: Verify features meet business requirements before release
- Static analysis: Catch code quality issues and security vulnerabilities in the CI pipeline
Kersten offered a stark competitive warning: “Those companies that don’t start automating now will get disrupted by the more lean startups.”
DevOps Success Requires a Quality Culture, Not Just Speed
Reducing technical debt is ultimately a cultural challenge as much as a technical one. Heusser described how organizations that consistently reward new features while ignoring code quality create a self-reinforcing cycle: “New features are rewarded, and there are not clear consequences for bad code. The net result is the code base continues to get worse, and there is a great deal of baggage added, which slows down forward progress.”
Engineering leaders who want to break this cycle need to make quality a measurable, visible, and rewarded outcome—not an afterthought. That means tracking defect escape rates, cycle time for bug resolution, and test coverage as first-class engineering metrics alongside delivery velocity.
Technical debt is inevitable in fast-moving software organizations. The teams that manage it well are those who treat debt reduction as ongoing engineering work—scheduled, prioritized, and resourced—rather than a crisis to address only when it becomes impossible to ignore.