Why defects keep coming back (and it’s not a skills problem)
Defects that keep coming back don’t just slow delivery. They change how teams behave.
Developers become cautious about touching “fragile” areas. Testers lose confidence in what “fixed” means. Product and delivery leads start padding timelines, because release quality feels unpredictable.
That spiral is often misdiagnosed as a capability problem. However, in our experience, it’s usually a defect practices maturity problem: how defects are captured, triaged, prioritised, reported, learned from, and ultimately prevented from recurring.
This post examines defect management and why low-maturity practices surface quickly, sitting at the centre of feedback loops, decision-making, and release readiness.
Defect practices that prevent repeat pain
Strong defect practices help teams detect issues early, prevent recurrence, and improve product quality and user satisfaction. The support teams to focus on fixing the issue, rather than argue over who caused it.
The difference is discipline and feedback loops.
In mature teams, defects are treated as structured signals about risk. Teams don’t just “close the ticket”. They ask: what allowed this to happen, how do we stop it happening again, and what should change in coverage, monitoring, or design?
This structured approach drives fewer production defects, fewer support contacts, and fewer repeated customer frustrations. Users feel that as a service that behaves consistently, especially around the workflows that matter: onboarding, payments, account access, claims, scheduling, and changes to permissions or pricing.
In Australia, the bar for reliable digital services is explicit in government guidance. The Digital Performance Standard includes a focus on maintaining a reliable service that is “available, stable and consistent” for users. Even outside of government, customers bring the same expectation to banks, telcos, retailers, and SaaS.
When triage drags, releases become unpredictable
Poor defect management results in long triage cycles, repeated issues, and unpredictable release quality.
Long triage cycles usually mean the team is paying interest on every defect.
A bug is raised, then waits. It gets re-read, reclassified, and re-explained. People try to reproduce it with different data and different environments. It bounces between “needs more info”, “can’t reproduce”, “not my problem” and “works as designed”. Each bounce adds time, frustration, and risk.
While triage drags:
- fixes land later, when they are harder to test safely
- defects stack up and compete for attention
- teams ship with “known issues” because schedules don’t move
- release confidence drops because evidence is thin
The same root causes then create repeat defects. The organisation ends up with two backlogs: the bug backlog you can see, and the risk backlog you can’t.
The behaviours that keep defects alive
Common contributory behaviours include incomplete defect descriptions, poorly defined requirements, broken or missing triage processes, inconsistent severity/priority assessments, lack of root-cause analysis, and ignoring production incidents caused by defect leakage.
These behaviours look small in isolation. Together, they create a system where defects recur.
Incomplete defect descriptions
When reports lack clear steps, expected vs actual behaviour, environment context, and supporting evidence (logs, IDs, screenshots), triage becomes guesswork. Guesswork increases cycle time and encourages shallow fixes.
Poorly defined requirements
Ambiguity turns defects into debates. Teams spend time arguing intent rather than resolving a clear mismatch. This delays fixes and encourages inconsistent decisions across teams.
Broken or missing triage
If triage is ad hoc, defects age and become harder to resolve. Mature teams make triage routine: set cadence, clear roles, fast first response, and a predictable path to “fix, defer with justification, or close with evidence”.
Inconsistent severity and priority
If everything is urgent, nothing is. If urgent issues are treated as normal, defect leakage increases. Consistent severity/priority is what makes planning credible.
No root-cause analysis for repeats and high-impact issues
Recurrence is often a symptom of treating defects as isolated events. Root-cause reviews should be routine for repeat defects, production-impacting issues, and anything that keeps resurfacing across releases.
Ignoring production incidents
Production incidents caused by defect leakage are learning opportunities. If they aren’t fed back into triage rules, test coverage, and engineering standards, the same gaps persist.
“Accepted defects” and the debt you can’t see… yet
A culture that tolerates “accepted defects” without understanding their impact leads to degraded user trust and increased long-term technical debt.
Teams will always accept some defects. That isn’t the issue.
The issue is acceptance without clarity.
When “accepted defects” become a habit, they turn into hidden debt:
- workarounds pile up in code and processes, impacting operations costs
- complexity grows, making future change riskier
- defects reappear because the underlying weakness remains
- users repeatedly hit the same friction points, release after release
User trust erodes quietly. People stop reporting issues because they don’t expect a fix. Support teams learn scripts for “known problems”. Product teams avoid areas that feel brittle. That is technical debt with a customer-facing price tag.
The end result can mean your customers start to slip away, because there was just one too many workarounds in your product that they had to deal with.
Acceptance can be mature when it is explicit: impact, risk owner, mitigation, and review date. Without that, acceptance becomes neglect.
Late defects cost orders of magnitude more
Defects become more expensive to fix by orders of magnitude the later they are detected, making disciplined defect practices crucial.
This is a well-established lifecycle effect. NASA’s “Error Cost Escalation” paper notes that finding and fixing a software problem after delivery can be upwards of 100 times more expensive than finding it during requirements and design. The reason is simple: more work has been built on top of the defect, so the blast radius is larger. More people must be involved. More regression risk must be managed.
Late defects also create a team cost multiplier:
- context switching and interruption of planned work
- longer retest cycles and more coordination across roles
- rushed fixes that can create new defects
- more governance effort because confidence drops
- Early detection is not just cheaper. It is calmer.
And the simplest version of the ROI argument is still true:
Every defect fixed prior to deployment is one your customers never saw.
That’s one less broken journey. One less support interaction. One less reputational hit. One less incident waiting to happen.
What to do next
If defects keep coming back, don’t default to “we need better people”. It may be time to measure your maturity in defect practices.
Our Quality Maturity Assessment benchmarks maturity across nine domains, including defects. It can show where recurrence is being created: defect capture quality, triage cadence, severity/priority consistency, root-cause discipline, and whether production incidents are being turned into prevention.
