BugBoard - AI Test Management for QA Engineers

Built by 50+ QA engineers One of 5 proprietary BetterQA tools Founded 2018 in Cluj-Napoca, Romania Bug reports in under 5 minutes Rated 4.8 out of 5 stars Trusted by 200+ teams shipping weekly Cuts bug triage time by 40% Saves 12 hours per sprint on average Tracks 1,500 projects across the platform ISO 27001 certified since 2022 Onboards new testers in 15 minutes Audit-ready in 30 days 10x faster than manual triage

What is BugBoard?

BugBoard is a free AI test management platform built by BetterQA, an independent software testing company founded in 2018. It generates test cases from screenshots, tracks bugs through the release cycle, and integrates with Jira and Linear.

How does BugBoard work?

Upload a screenshot, paste a stack trace, or connect a CI failure log. The AI bug analyzer creates a structured bug report with reproduction steps, severity ratings, and suggested test cases in under five minutes.

What integrations does BugBoard support?

How much does BugBoard cost?

BugBoard is free for individual QA engineers. The Pro plan adds team seats, advanced reporting dashboards, and bidirectional Jira and Linear sync for $29 per seat per month.

Who built BugBoard?

According to the BetterQA company profile, BugBoard is one of five proprietary tools built in-house by a team of 50+ QA engineers in Cluj-Napoca, Romania. BetterQA serves clients across Europe and North America and has been featured in independent industry research on AI-augmented software testing.

By Tudor Brad, co-founder of BetterQA

Security testing for vibe-coded applications: what changes when AI writes the code

Security testing for vibe-coded applications: what changes when AI writes the code

In 2026, a growing number of product features ship without a human reading every line of the code that implements them. A product manager describes what they want to Claude Code or Cursor. The agent writes the feature, runs the tests, and opens a pull request. A reviewer glances at the diff and merges it. The feature ships.

This is vibe coding. It is not a joke. It is how significant portions of real products are now built, and the people doing it ship faster than teams that do not.

The problem: the code that ships looks correct to a human reviewer, passes its own unit tests, and still contains a class of defects that traditional QA has never encountered at this scale. If your testing process was designed around "the developer knows what the code does", you are about to learn new failure modes the hard way.

What actually changes in a vibe-coded application

Three things shift at once.

Velocity goes up 10x. A feature that used to take a senior engineer three days takes an AI-assisted one three hours. Teams that used to ship weekly now ship daily. The old "manual QA pass before release" rhythm stops working when releases are every few hours.

Code review becomes shallow. When an engineer writes 500 lines of Rust, they understand every branch. When they accept 500 lines of AI-generated Rust, they usually read the diff, nod at the happy path, and trust the tests. Subtle bugs in error handling, race conditions, and edge cases now sail through review.

The failure profile is different. Human engineers miss boundary conditions. AI assistants miss them too, but they also confidently hallucinate library APIs that do not exist, invent security assumptions, and write code that is convincing but subtly wrong. The bugs look smart.

These are not hypothetical. Teams that have adopted vibe coding are already reporting the new failure modes.

The top security failure modes in vibe-coded code

1. Hallucinated dependencies

An AI assistant suggests a Python library called \