Why your test suite keeps growing but coverage keeps shrinking
Your regression suite is bigger than it was last quarter. But coverage feels thinner.
Teams run more tests, spend more time triaging failures, and still get surprised late in delivery.
This is a common quality maturity trap: volume increases while protection declines.
One of the most frequent contributors to this unevenness is Design & Coverage. When quality feels inconsistent across teams or releases, it’s rarely due to a lack of testing effort. More often, testing is designed and applied unevenly. In fact, uneven Design & Coverage environments are often characterised by high effort and low confidence. Teams are busy, suites are large, and activity is visible, but assurance is weak because design decisions were never anchored to business risk.
Traceability is missing from the conversation.
Design & Coverage shapes whether testing provides high-signal assurance, or simply generates more activity without addressing risk. When this area is weak, teams can appear busy while still missing the issues that matter most.
Meaningful coverage beats more tests
Good test design ensures coverage is meaningful, traceable, efficient, prioritised, and aligned to risk. That’s how mature teams reduce testing effort while increasing confidence.
Meaningful coverage maps to business processes, not just story acceptance criteria. If your product supports onboarding, payments, claims, identity, scheduling, or reconciliation, the risks live in exceptions and handoffs. Coverage that only validates UI flows will miss the failure modes customers actually trigger.
Efficient coverage avoids duplicated intent. Disciplined teams consolidate tests that prove the same thing, retire low-value checks, and keep the suite small enough to maintain.
Prioritised coverage acknowledges reality. You can’t test everything at the same depth. Katalon’s State of Software Quality Report 2024 shows “lack of time to ensure quality” as a leading challenge, cited by 48% of respondents. Risk alignment is what helps you spend depth where failure is costly and use lighter checks plus monitoring where it isn’t.
The result is confidence based on evidence, not suite size.
How suites grow while confidence drops
Poorly designed tests lead to gaps in coverage, duplicated scenarios, false positives/negatives, and increased maintenance.
Gaps form because teams test what is easiest to automate rather than what is most likely to fail. Duplicates appear because new tests are added after incidents without rationalising what already exists. The suite gets longer, but coverage quality degrades. Linkage between what is tested and the business requirements goes missing, further widening the gap.
False signals accelerate the decline. False positives waste engineering hours and train teams to ignore failures. False negatives create a dangerous calm where everything looks green while risk is untested.
Flakiness is often the trigger. The International Flaky Test Workshop summary describes flakiness as a persistent challenge in projects that depend on automated testing and CI. A 2025 empirical study also reports measurable time spent repairing flaky tests, with a concrete monthly cost example in an industrial case study. That is direct ROI leakage: time spent repairing the suite is time not spent improving the product.
Over time, this pattern quietly erodes trust in testing itself. Teams stop believing green results. Failures are treated as noise. Success is treated with suspicion. What should be a decision-making signal becomes something teams work around rather than rely on. When trust in test outcomes disappears, confidence collapses, even as effort (and cost!) continues to increase.
The three habits that quietly shrink coverage
Three contributory behaviours cause suite growth while coverage shrinks.
Writing tests without understanding business processes creates surface validation. The feature works in a demo but fails when real workflows include exceptions, partial failures, data variation, and cross-system state transitions. The suite grows, but it protects the wrong thing.
Happy-path bias creates a green suite that doesn’t represent the real world. Repeat incidents often live in boundaries, permissions, retries, timeouts, and concurrency. If those patterns aren’t designed into coverage, expansion doesn’t improve protection.
Lack of traceability makes drift invisible. Without it, teams can’t answer which tests protect the highest business risks, what should change when requirements change, and where coverage is duplicated or missing. The test suite cannot be prioritised, and therefore all tests must be run every release, rather than have targeted scope based on impact analysis. A 2024 systematic literature review on traceability highlights ongoing benefits and persistent problems, reinforcing that it remains challenging to implement consistently. When traceability is weak, teams keep everything “just in case”, so suites grow by default.
Design techniques that cut tests, not assurance
Bloated test suites are rarely caused by too little testing. They are caused by unfocused and unselective testing. When teams don’t use structured design techniques, they compensate by adding more checks “just in case”. Over time, this creates large, expensive suites that still miss the risks that matter most.
Structured design techniques don’t add ceremony. They add intent, and reduce overall effort.
Risk-based design aligns depth of testing to business impact, ensuring critical customer journeys receive strong protection while lower-risk paths rely on lighter checks and monitoring. Boundary analysis and negative testing focus coverage where defects actually cluster – at minimums, maximums, and just-outside-valid values that happy-path testing rarely exercises. Pairwise and combinatorial test design reduce interaction risk by deliberately selecting meaningful combinations, rather than attempting to test everything.
Used together, these techniques change how suites grow. Tests are added deliberately, duplication is avoided, and low-value checks are retired. Traceability is built into the design process, so tests can be used selectively and with intent – not as an all or nothing block. The result is fewer tests, faster feedback, and stronger confidence – not because more work is done, but because the right work is done.
When coverage lies, delivery pays
Bad coverage practices eventually erode trust in testing and cause surprises late in delivery.
Once trust drops, everything slows down. Teams rerun tests “just in case”. Hardening expands. UAT becomes a defect-finding phase. Late surprises trigger rework, additional cost, and displace planned delivery.
In Australia, reliability expectations aren’t vague. For example, Government guidance for their digital services explicitly calls for services to be “available, stable and consistent”. Even outside government, customers apply the same expectation to banks, telcos, retailers, and SaaS. Reliability is assumed, and failure is remembered. And where it fails? Customers seek out the competition.
That’s why Design & Coverage is an ROI issue. A bloated suite with shrinking coverage drives higher engineering cost through maintenance and triage, longer delivery cycles due to late surprises, higher operations costs due to leakage of defects to production, and increased governance overhead because evidence is weak and confidence is low.
It also creates uneven quality across teams. Some teams have clean, risk-aligned coverage and can release with confidence. Others operate in constant uncertainty.
What to do next
Design & Coverage is just one of the nine domains in our Quality Maturity Assessment. It helps you measure whether your suite is growing for the right reasons, whether coverage is truly risk-aligned, and where drift is creating hidden gaps and late surprises.
