Best Software Testing Practices for Growing SaaS Companies
The support ticket said the export button was broken. Engineering tried it on three accounts and couldn’t reproduce the problem. Only after a customer sent over a recording did someone notice the date range: six years. Test accounts held a few weeks of sample data. The export had probably been failing for months, but nobody inside the company had a reason to ask for that much history.
This is an ordinary problem in an older SaaS product. The application that developers see in staging bears only a passing resemblance to the one some customers have been using since launch. Accounts accumulate strange settings. Third-party tools change their behavior. A feature built for twenty customers now serves twenty thousand.
The Release Ticket Covers Less Than You Think
A developer adds a second payment provider. The new checkout works; the test card is charged, and a receipt appears. Two days after release, subscriptions created before the migration begin renewing at the wrong price.
Nobody changed the renewal screen. The pricing rules, however, sit in a shared service that the new checkout also uses. A review focused on the new payment method wouldn’t necessarily catch that connection.
Before a risky release, an engineer familiar with the codebase should check which other parts of the application depend on the changed feature. Some connections are easy to overlook, especially in older code that few people on the team understand. The review should cover:
- Shared data and services;
- Existing accounts and subscriptions;
- User roles and permissions;
- Scheduled jobs and background tasks.
These checks matter because a small change can affect features nobody planned to touch. Updating account roles may disrupt overnight reports, while editing an address form can interfere with tax calculations.
For routine updates, a compact regression suite is useful. For changes touching money or access, running a wider set of checks is usually worth the delay.
A Customer Can Use a Feature Once a Year and Still Depend on It
Traffic reports tend to make popular screens look important. Those screens deserve attention, but the rarely used parts of an application may carry considerable risk.
Take a payroll tool. Most employees open it to view payslips. Administrators might download a year-end report only once. If that report is wrong, the company has a far bigger problem than a slow payslip page.
Testing priorities should reflect the cost of a failure and how difficult it would be to undo. A misplaced label can wait. Duplicate charges, missing records, and permissions that expose confidential files generally cannot.
Customer support often knows which problems create real disruption long before a product dashboard shows anything unusual. Complaints arrive in untidy language, though. “My account is wrong” might describe a rounding error, an outdated balance, or a failed sync. Someone has to inspect the case rather than filing everything under “billing issue.”
Automation Gets Old Too
A redesign changes button identifiers, breaking dozens of browser tests overnight. Engineers fix the scripts, but further interface updates keep causing failures. Eventually, even a failed test run gets little attention.
Automated checks need owners. Without maintenance, passing tests can create false confidence while failing tests lose credibility. Teams should regularly review a few things:
- Tests that fail without real bugs;
- Scripts affected by interface changes;
- Critical workflows missing coverage;
- Checks that no longer serve a purpose.
A billing test might run every night to verify that subscriptions renew at the correct price. Permission checks can run automatically as well. Once the expected result is known, there’s little reason to repeat these tasks by hand.
Onboarding is less predictable. A person may spend several minutes filling out a registration form, only to reach a screen asking for information they don’t have. They go back, change a few answers, and try again. Nothing is technically broken. The fields work, the buttons respond, and the system accepts valid entries. Yet getting through the process has become unnecessarily difficult.
Older customer accounts present a different problem. One business might still have a subscription from 2021, while another has billing records that went through two database migrations. Developers rarely use accounts like these during routine testing. They create new ones, add a few sample transactions, and move on. Bugs affecting long-term customers can remain hidden even when the automated test suite reports no failures.
Who Can Stop a Release?
In a small product team, engineers often test their own changes. That is workable until deadlines shorten and nobody agrees on what “checked” means. One person thinks it covers the intended action. Another assumes failed payments, expired sessions, and older browsers were included.
Bringing in a QA specialist can help with unfamiliar workflows and overlooked cases. When a release calls for specific expertise or more capacity than the team has, external software testing services are another option. Either way, the people doing the work need access to requirements, representative accounts, and developers who can answer questions.
A bug marked “minor” by engineering may block an entire customer department. Someone needs to make that call with more information than a screenshot and a severity label.
In Production, Services Misbehave
A payment test can pass without telling the team much about what happens when something goes wrong. The gateway confirms the transaction, the booking appears, and everyone moves on. That’s the easy case. Real payment services don’t always respond the way they should. Messages arrive late, connections drop, and sometimes the same confirmation comes through twice.
Say a customer books a room and pays for it. The payment goes through, but the gateway doesn’t receive an acknowledgment from the booking system. So it sends the confirmation again. If nobody tested what happens next, the system might treat that second message as a new payment and create another reservation. The original test was green. The customer now has two bookings.
The test environment probably won’t reveal this unless someone deliberately sends a duplicate event. There are other situations worth reproducing, too. A file upload gets interrupted halfway through, or an API request times out even though the provider has already completed the operation. Older customer accounts can cause trouble for entirely different reasons, especially when their records have survived several database changes.
A report that loads in two seconds with a few sample records might take much longer with years of customer data. Testing it against a nearly empty database won’t reveal much about that workload.
These cases are easy to postpone because they don’t happen during the standard demonstration. They also tend to show up at inconvenient times, when support has incomplete details, and the engineer who knows that integration is away.
After the Incident
One customer reports a problem. A developer fixes it and moves on. A month later, three customers report something similar, but their tickets have different names and live in different queues.
Look for patterns in production incidents rather than treating each one as an isolated surprise. Maybe a test account needs older data. Maybe an integration needs idempotency checks. Perhaps support should collect browser versions or timestamps before escalating an issue.
The last incident review ended with an odd request: make a test account with six years of transactions. Nobody had thought to create one before.