Modern software development can feel slow even when developers are writing code faster: old systems consume attention, tools leave gaps in context, AI-generated changes need careful validation, and sustained pressure can erode focus. These five recurring pain points show up in different surveys of different groups, so they are not a universal ranking. The practical response is to fix the source of friction—not simply ask people to move faster.
1. Technical debt and legacy maintenance
When developers spend time deciphering old code, untangling dependencies, or fixing problems caused by earlier shortcuts, less capacity remains for new work. In Sonar’s 2026 State of Code Developer Survey, 41% of respondents placed managing technical debt among their top five sources of development toil. Debugging legacy or poorly documented code followed at 32%, while 23% cited finding information or understanding existing systems. These figures describe that survey’s respondents, not every development team.
How to make progress
- Make the debt visible. Record recurring sources of rework, fragile modules, and code that is difficult to understand. Prioritize by the cost they impose on planned changes, incidents, or onboarding—not by how untidy a file looks.
- Reserve capacity for improvement. Include debt reduction in normal planning rather than treating it as work that can happen only after every feature is complete.
- Refactor with safeguards. Add or strengthen tests around the behavior being changed, make focused improvements, and document non-obvious system behavior so the next person does not have to rediscover it.
Sonar respondents reported positive AI effects in documentation, test coverage and debugging, and refactoring, alongside negative effects. AI may assist with these tasks, but generated changes still need review and verification; it is not an automatic debt-removal solution.
2. Fragmented workflows and missing context
Developers lose time when the information needed to make a change is scattered across tools, teams, and handoffs. A ticket may not show why a decision was made; a code review may lack the production context; or delivery status may be difficult to trace across a toolchain. This is a workflow-design problem, not evidence that individual developers are failing to communicate.
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Atlassian’s 2025 State of Developer Experience report, based on 3,500 developers and managers surveyed with Wakefield Research, describes teams perceiving AI-related time gains while also reporting greater organizational inefficiencies. GitLab’s June 2026 survey, conducted by The Harris Poll, identified fragmented toolchains as a barrier to control and traceability. Only 28% of its respondents said their software development lifecycle tools were fully integrated with shared data and workflows. These surveys ask different questions of different populations, so their figures should not be treated as directly comparable.
How to reduce the friction
- Connect the work’s context. Make requirements, design decisions, code changes, reviews, and delivery status findable from the places where people act on them.
- Improve handoffs. Agree what information must accompany a change—such as its purpose, dependencies, validation, and operational owner—and keep that information current.
- Review the toolchain as a workflow. Identify duplicate entry, missing integrations, and status that must be manually reconstructed. Fix the highest-cost breakpoints before adding another tool.
Gartner advises engineering leaders scaling AI use to favor an AI application-development platform or a strong ecosystem over a collection of disparate vendors when reuse and consistency matter. That is Gartner’s recommendation for scaling, not a universal rule that every team must use one platform.
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3. AI-generated code that is difficult to trust and maintain
AI-generated code can look plausible while still being unreliable, duplicative, hard to maintain, incompatible with existing systems, or risky from a security perspective. In Sonar’s 2026 developer survey, 88% of respondents reported at least one negative technical-debt impact from AI. Specifically, 53% cited code that looked correct but was unreliable, and 40% cited unnecessary or duplicative code. These are reported experiences and perceptions, not a controlled estimate that AI caused a particular amount of debt.
How to use AI without surrendering judgment
- Review behavior, not just appearance. Check whether a change meets the requirement, handles relevant edge cases, and fits established patterns and interfaces.
- Test the change at the right level. Run relevant unit, integration, and security checks; add tests where the behavior or regression risk warrants them.
- Look for hidden maintenance costs. Remove redundant code, question unfamiliar dependencies, and check compatibility with the surrounding system before merging.
- Keep responsibility with the team. Treat AI as a drafting aid, not as the accountable author or approver of production behavior.
DORA’s 2025 report, drawing on nearly 5,000 technology professionals and more than 100 hours of qualitative data, characterizes AI as an amplifier of organizational strengths and dysfunctions. In practice, a tool may help a team with clear standards and feedback loops while compounding problems where those foundations are missing.
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4. Review, integration, and governance bottlenecks
Faster code production does not guarantee faster delivery. If more changes arrive than a team can review, test, integrate, and safely release, the queue simply moves downstream. GitLab’s June 2026 survey found that 85% of respondents agreed AI had shifted the bottleneck from writing code to reviewing and validating it. In the same survey, 79% said individual productivity had improved while overall delivery had not accelerated at the same pace. These are survey responses, not a universal measure of delivery performance.
Governance is part of the same bottleneck. GitLab reported that 92% of respondents faced some governance challenge with AI-generated code, and 80% said AI tools had been adopted faster than governance policies. Separately, Gartner’s May 2025 survey found that 77% of engineering leaders identified building AI capabilities into applications as a significant or moderate pain point, while 71% said the same of using AI tools to augment engineering workflows. The figures reflect each source’s own survey framing and population.
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How to keep validation and accountability workable
- Make review capacity explicit. Track where changes wait and whether the delay is caused by unclear ownership, oversized changes, missing tests, or scarce specialist review.
- Keep changes reviewable. Break work into coherent units and include the intended behavior and validation evidence so reviewers can assess the change without reconstructing its purpose.
- Define AI accountability. GitLab frames accountability around where code came from, what it was meant to do, and who is responsible once it is in production. Maintain enough context to answer those questions for consequential changes.
- Set policy alongside adoption. Clarify acceptable data use, required review and testing, and ownership before relying on AI-generated code in production. A governance tool alone cannot replace clear responsibilities and working practices.
5. Burnout, reduced focus, and unsustainable expectations
Software work requires sustained attention, but constant urgency, interruptions, and pressure to absorb more work can make concentration and motivation harder to maintain. State of Devs 2026, published by Devographics, reports that 62% of respondents had experienced burnout at some point in their career. Respondents also described reduced motivation, cynicism, difficulty focusing, and emotional exhaustion. These are self-reported survey findings, not clinical assessments or a diagnosis of any individual.
How to address the conditions behind the strain
- Look for workload patterns. Examine interruptions, on-call demands, deadline churn, and work that repeatedly displaces planned tasks.
- Protect time for focused work. Reduce avoidable meetings and handoffs, and make priorities clear enough that developers are not expected to treat every request as urgent.
- Correct unsustainable expectations. Revisit scope, staffing, and delivery commitments when teams repeatedly rely on overtime or skipped maintenance to meet them.
- Respond without blame. Treat loss of focus or motivation as a reason to inspect team conditions and offer appropriate support, not as proof that someone needs to optimize their personal productivity.
The survey establishes that these concerns are reported by developers; it does not determine which management intervention will work for a particular team.
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Start with the recurring symptom and locate where the friction sits. A legacy-code problem calls for codebase understanding and safer maintenance; handoff delays point toward shared context and tool integration; a growing review queue calls for validation capacity and clear ownership; persistent loss of focus calls for examining workload and team conditions. Some problems overlap, so a single tool or policy is unlikely to resolve every cause.
When considering AI, assess the working environment as well as the tool: whether the team has understandable code, reliable tests, connected workflows, accountable review, and realistic capacity. DORA’s 2025 findings support treating organizational practice as a major factor in whether AI amplifies strengths or dysfunctions. Improvements should be judged by outcomes such as less rework, clearer traceability, dependable changes, and sustainable delivery—not code volume alone.
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