Manually Triggered Tests

What This Looks Like

Your team has tests. They are written, they pass when they run, and everyone agrees they are valuable. The problem is that no automated process runs them. Developers are expected to execute the test suite locally before pushing changes, but “expected to” and “actually do” diverge quickly under deadline pressure. A pipeline might exist, but triggering it requires navigating to a UI and clicking a button - something that gets skipped when the fix feels obvious or when the deploy is already late.

The result is that test execution becomes a social contract rather than a mechanical guarantee. Some developers run everything religiously. Others run only the tests closest to the code they changed. New team members do not yet know which tests matter. When a build breaks in production, the postmortem reveals that no one ran the full suite before the deploy because it felt redundant, or because the manual trigger step had not been documented anywhere visible.

The pattern often hides behind phrases like “we always test before releasing” - which is technically true, because a human can usually be found who will run the tests if asked. But “usually” and “when asked” are not the same as “every time, automatically, as a hard gate.”

Common variations:

• Local-only testing. Developers run tests on their own machines but no CI system enforces coverage on every push, so divergent environments produce inconsistent results.
• Optional pipeline jobs. A CI configuration exists but the test stage is marked optional or is commented out, making it easy to deploy without test results.
• Manual QA handoff. Automated tests exist for unit coverage, but integration and regression tests require a QA engineer to schedule and run a separate test pass before each release.
• Ticket-triggered testing. A separate team owns the test environment, and running tests requires filing a request that may take hours or days to fulfill.

The telltale sign: the team cannot point to a system that will refuse to deploy code if the tests have not passed within the last pipeline run.

Why This Is a Problem

When test execution depends on human initiative, you lose the only property that makes tests useful as a safety net: consistency.

It reduces quality

A regression ships to production not because the tests would have missed it, but because no one ran them. The postmortem reveals the test existed and would have caught the bug in seconds. Tests that run inconsistently catch bugs inconsistently. A developer who is confident in a small change skips the full suite and ships a regression. Another developer who is new to the codebase does not know which manual steps to follow and pushes code that breaks an integration nobody thought to test locally.

Teams in this state tend to underestimate their actual defect rate. They measure bugs reported in production, but they do not measure the bugs that would have been caught if tests had run on every commit. Over time the test suite itself degrades - tests that only run sometimes reveal flakiness that nobody bothers to fix, which makes developers less likely to trust results, which makes them less likely to run tests at all.

A fully automated pipeline treats tests as a non-negotiable gate. Every commit triggers the same sequence, every developer gets the same feedback, and the suite either passes or it does not. There is no room for “I figured it would be fine.”

It increases rework

A defect introduced on Monday sits in the codebase until Thursday, when someone finally runs the tests. By then, three more developers have committed code that depends on the broken behavior. The fix is no longer a ten-minute correction - it is a multi-commit investigation. When a bug escapes because tests were not run, it travels further before it is caught. By the time it surfaces in a staging environment or in production, the fix requires understanding what changed across multiple commits from multiple developers, which multiplies the debugging effort.

Manual testing cycles also introduce waiting time. A developer who needs a QA engineer to run the integration suite before merging is blocked for however long that takes. That waiting time is pure waste - the code is written, the developer is ready to move on, but the process cannot proceed until a human completes a step that a machine could do in minutes. Those waits compound across a team of ten developers, each waiting multiple times per week.

Automated tests that run on every commit catch regressions at the point of introduction, when the developer who wrote the code is still mentally loaded with the context needed to fix it quickly.

It makes delivery timelines unpredictable

A release nominally scheduled for Friday reveals on Thursday afternoon that three tests are failing and two of them touch the payment flow. No one knew because no one had run the full suite since Monday. Because tests run irregularly, the team cannot say with confidence whether the code in the main branch is deployable right now.

The discovery of quality problems at release time compresses the fix window to its smallest possible size, which is exactly when pressure to skip process is highest. Teams respond by either delaying the release or shipping with known failures, both of which erode trust and create follow-on work. Neither outcome would be necessary if the same tests had been running automatically on every commit throughout the sprint.

Impact on continuous delivery

CD requires that the main branch be releasable at any time. That property cannot be maintained without automated tests running on every commit. Manually triggered tests create gaps in verification that can last hours or days, meaning the team never actually knows whether the codebase is in a deployable state between manual runs.

The feedback loop that CD depends on - commit, verify, fix, repeat - collapses when verification is optional. Developers lose the fast signal that automated tests provide, start making larger changes between test runs to amortize the manual effort, and the batch size of unverified work grows. CD requires small batches and fast feedback; manually triggered tests produce the opposite.

How to Fix It

Step 1: Audit what tests exist and where they live (Week 1)

Before automating, understand what you have. List every test suite - unit, integration, end-to-end, contract - and document how each one is currently triggered. Note which ones are already in a CI pipeline versus which require manual steps. This inventory becomes the prioritized list for automation.

Step 2: Wire the fastest tests to every commit (Week 2)

Start with the tests that run in under two minutes - typically unit tests and fast integration tests. Configure your CI system to run these automatically on every push to every branch. The goal is to get the shortest meaningful feedback loop running without any human involvement. Flaky tests that would slow this down should be quarantined and fixed rather than ignored.

Step 3: Add integration and contract tests to the pipeline (Weeks 3-4)

After the fast gate is stable, add the slower test suites as subsequent stages in the pipeline. These may run in parallel to keep total pipeline duration reasonable. Make these stages required - a pipeline run that skips them should not be allowed to proceed to deployment.

Step 4: Remove or deprecate manual triggers (Week 5)

Once the automated pipeline covers what the manual process covered, remove the manual trigger options or mark them clearly as deprecated. The goal is to make “run tests manually” unnecessary, not to maintain it as a parallel path. If stakeholders are accustomed to requesting manual test runs, communicate the change and the new process for reviewing test results.

Step 5: Enforce the pipeline as the deployment gate (Week 6)

Configure your deployment tooling to require a passing pipeline run before any deployment proceeds. In GitHub-based workflows this is a branch protection rule. In other systems it is a pipeline dependency. The pipeline must be the only path to production - not a recommendation but a hard gate.

Measuring Progress

Related Content

• Testing fundamentals
• Deterministic pipeline
• Pipeline architecture
• Metrics-driven improvement