Do these 3 things before closing this tab:
1Clear out junk files and repair common Windows errors2Scan for outdated or missing drivers - takes under a minute3Repair Windows errors before they cause bigger problemsA sudden wave of bug reports after adopting AI coding assistants does not, by itself, show that the code has become less reliable. If a team produces and ships more changes in less time, defects may surface in a tighter cluster—even if the underlying defect rate per change has not risen. That is a plausible explanation, not proof that AI has no effect on defect rates.
Why bugs can arrive sooner without a higher defect rate
Implementation speed is only one part of shipping software. Review, testing, integration, product decisions, and debugging still take time. If more code reaches users in a shorter period, the team may encounter the consequences of skipped checks or unresolved design questions sooner than it would have when releases were spread across months.
As an Amazon Associate I earn from qualifying purchases.
Otto Hilska, founder and CEO of Swarmia, put the timing argument this way in LeadDev on August 25, 2026: “What’s changed is the speed. Skip any of this and you used to get away with it for a couple of months. Now, you find out in the second week.” The point is about when problems become visible; it is not evidence that AI-generated code has no effect on how many defects are introduced.
The Tool Desk
Outbyte PC Repair FREERepair Windows errors before they cause bigger problemsFix Now →Outbyte Driver Updater FREEScan for outdated or missing drivers - takes under a minuteDriver Scan →Swarmia reported that, in its platform data covering more than 1,450 engineering organizations, median lines changed per pull request roughly doubled from Q1 2025 to Q1 2026. For organizations with at least 1,000 pull requests in each period, Swarmia reported a 97.5% median increase. Those figures describe pull-request batch size—not bugs, defect rates, or causation—and come from Swarmia’s own data, not an established independent or representative study. Swarmia’s analysis of AI coding tools and productivity
How to tell whether quality is actually getting worse
Do not use a raw bug or incident count as a verdict. A higher count can accompany a higher volume of shipped work, so compare quality signals with delivery volume and the size of changes. Look at trends across comparable periods or teams, rather than reading one busy release window in isolation.
- Change failure rate: Track how often changes lead to failures, using a consistent definition and comparable deployment scope.
- Time to recovery: Measure how quickly the team restores service or resolves a production problem.
- Pull-request batch size and review time: Larger changes can be harder to review and carry more risk; interpret their size alongside how long review takes and what happens after deployment.
- Maintenance share: Observe how much engineering capacity goes to keeping systems working and addressing technical debt.
- Deployment confidence: Ask engineers whether they feel comfortable shipping and what affects that confidence.
No single measure establishes overall quality. Definitions, work mix, and measurement coverage matter, so treat these signals as a set and check whether apparent changes persist.
Rank #2
How to make room for fixes while feature work continues
Choose a predictable share of capacity for maintenance and technical debt instead of relying on occasional feature pauses to create time for quality work. The appropriate allocation depends on the product and team; Hilska’s recommendations do not supply a universal percentage. Make the trade-off visible when planning work so that fixing recurring causes is a planned responsibility rather than leftover capacity.
Hilska also recommends treating review as an engineering conversation, writing tests as implementation proceeds, pairing when the cost of being wrong is high, and investing in design discussion. These practices help teams examine assumptions and risks while changes are still understandable, rather than treating a fast implementation as a finished decision.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What can become the bottleneck after coding speeds up
Faster implementation does not make product choices or system context arrive faster automatically. Teams still need time to understand user needs, agree on behavior, and learn how a change interacts with the wider system. Hilska recommends giving engineers room to build product understanding and enabling product managers to prototype where useful, so implementation speed does not simply move the queue to decision-making.
When comparing teams or periods, pair throughput with batch size, review time, change failure rate, recovery time, maintenance effort, and shipping confidence. A team that ships more changes and reports more bugs may be producing more work, encountering defects sooner, experiencing a real quality decline, or some combination. The measures help distinguish those possibilities; the bug count alone cannot.
Quick Recap
Best Value
- Used Book in Good Condition
Product prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on Amazon at the time of purchase will apply.
