Why We Review Test Cases the Same Way We Review Pull Requests

The team behind Evaficy Smart Test built a peer-review workflow into its testing tool that treats test cases like pull requests, requiring a second reviewer to check both technical soundness and business alignment before execution. The workflow routes all pending test cases through a single review queue and automatically reverts an approved case to draft if it is edited afterward. The team says the habit matters more as AI-generated test suites can appear thorough while missing the condition the acceptance criteria actually cared about.

Code gets a second pair of eyes before it reaches production, as a matter of course, that habit is so normal by now that skipping it would feel strange. Test cases rarely get the same treatment: write them, run them, same person, same sitting. It's worth borrowing the code-review habit here too, especially now that a good chunk of test cases start as AI output. That's exactly the workflow we ended up building into Evaficy Smart Test, and we wrote up the full mechanics of it here: The Expert Validation Workflow https://app.evaficy.com/expert-validation-workflow . The short version, and the general idea behind it, is worth sharing on its own. The person who wrote a test case already has an answer in their head for how the feature is supposed to behave, and that answer quietly shapes every step they write. A reviewer starts without that context, which is exactly what makes them useful: they read the acceptance criteria fresh and check whether the test case actually matches it, instead of matching what the author assumed it meant. This matters even more with AI-generated test suites. A generated set of cases can look thorough at a glance, covering the happy path and a few edge cases, while still missing the one condition the acceptance criteria actually cared about. A reviewer catches that in a couple of minutes, well before anyone spends a test run executing cases that were checking the wrong thing. The useful version of this review is composed of two separate questions, ideally asked by two different people: is this technically sound right steps, right preconditions, actually testable , and does it reflect what the business asked for. A test case can pass the first check and fail the second, or the other way round, and catching either one before execution is cheaper than catching it after. In Evaficy, reviewers work from a single queue rather than hunting through individual scenarios: every pending test case across a project shows up in one place, and a reviewer can approve or reject a whole scenario in one action when everything in it is ready, or go case by case when it isn't. One detail we're fond of: if someone edits a test case after it's already been approved, it quietly drops back to a draft state rather than staying marked as approved. It sounds small, but it closes an easy gap — a step gets tweaked after sign-off, and without this it would just sit there, technically "approved," describing something slightly different from what was actually reviewed. You don't need our particular tool to adopt the habit, pick a reviewer, review before the first run, and keep it lightweight. If you're curious about the exact states, who reviews what, and what happens after a decision in our case, it's all written up here: The Expert Validation Workflow https://app.evaficy.com/expert-validation-workflow . One thing we're still going back and forth on internally: should AI-generated test cases get a stricter review than human-written ones, or does the same two-lens check hold up either way? Genuinely curious how other teams draw that line.