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5 QA pain points — and how Lepta QA solves them

Spec drift. Ad-hoc tracking. Flaky tests. Bug-to-PR limbo. Reporting fatigue. Here's how we attack each one.

Kwame BoatengMay 13, 20267 min read
5 QA pain points — and how Lepta QA solves them

Every team we onboard has a different stack — but the same five frustrations come up in nearly every kickoff call. Here they are, and how the platform tackles each.

A frustrated tester in front of two monitors

1. Spec drift

"The test cases were written in February. The product is in June. Nobody knows which ones still apply."

How we attack it: test cases live next to the surface they test. When a test case hasn't been touched and the linked screen has changed (new visual baseline, new selector, new bug filed against it), it's flagged as stale in the suite view. You can re-baseline or archive in one click.

2. Ad-hoc tracking

"Half the bugs are in JIRA, a third are in Slack threads, and a few exist only in someone's head."

How we attack it: a single inbox per project that ingests from JIRA, GitHub Issues, Linear, Slack, email, and Lepta QA's own tracker — with dedupe heuristics so the same bug doesn't appear three times. One source of truth, even when your sources of truth aren't.

3. Flaky tests

"We re-run the suite three times. If it passes on attempt #3, we ship."

How we attack it: every run is recorded with full traces. If a step passes-then-fails-then-passes, it's automatically marked as a flake candidate with a timeline view of all recent attempts. You see the actual signal instead of squinting at green/red status badges.

A graph showing flaky test trends

4. Bug-to-PR limbo

"The bug got filed, a fix got merged, but nobody verified that the verification actually verified anything."

How we attack it: bugs link bidirectionally to the PR(s) that fix them. When the fix merges, the bug auto-moves to "ready for QA" and the linked test case is queued. When the test passes on the deploy, the bug auto-closes. The QA-side handoff disappears.

5. Reporting fatigue

"I spend Friday afternoon writing the same release report I wrote two weeks ago."

How we attack it: AI-assisted release reports stitch the activity from the last release window into a draft you can edit. Bugs found and fixed, test runs passed/failed, sessions held, regressions caught — all timestamped, all sourced. You become an editor instead of a stenographer.

You shouldn't have to be a great writer to file a great release report.

The pattern

What ties these together is a single design principle: don't make humans bridge tools that should bridge each other. Most QA pain comes from manual stitching — ticket → recording → fix → verification → report. Each connection is a place where context gets lost. Closing those gaps is the entire point.

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