2 posts tagged with "buildinpublic"

Q: How do you currently get requirement traceability?
Which user stories and scenarios are covered by tests, and what’s failing?

A:
For most teams, it looks something like this:

User stories live in Jira.
Test cases live somewhere else.
The mapping between them lives in an Excel sheet that someone manually maintains.

That spreadsheet is periodically uploaded to a test management tool like PractiTest. Then test execution results are pushed via an API to get a view of coverage and failures.

It works—until it doesn’t.

The problem with today’s approach​

This is how requirement traceability is typically achieved today: a hodgepodge of tools stitched together with process and hope.

• Multiple sources of truth
• Manually maintained spreadsheets that inevitably go stale
• Fragile workflows that break as teams and test suites scale

No single system actually owns the full picture. Instead, teams spend time keeping artifacts in sync rather than improving product quality.

A simpler model with TestChimp​

In TestChimp, requirement traceability isn’t an afterthought. It’s built in.

You already author detailed user stories and break them down into meaningful test scenarios directly in the platform - with AI assistance that understands your product through your existing test scripts and documentation.

Linking tests to those scenarios is intentionally simple. In your test script, add a comment:

// @Scenario: <scenario title>

That’s it! TestChimp takes care of the rest:

• Automatically links tests to scenarios
• Tracks execution results across runs
• Aggregates outcomes at scenario, story, and suite level

You get clear, real-time dashboards that let you answer business relevant questions:

• Which user stories are missing test coverage?
• Which scenarios are currently failing?
• Which tests are flaky or unstable?

All without juggling multiple tools or maintaining brittle Excel sheets.

⸻

One system, end to end​

Instead of retrofitting traceability after the fact, TestChimp treats it as a first-class concept - connecting requirements, scenarios, and executions in one place.

• No spreadsheets.
• No manual syncing.
• Just a single system that understands what you’re building, how it’s tested, and where the gaps are.

LLMs sound like humans – so we often end up instructing them as if they experience the world like us.

But there’s a subtle difference – especially when used as Agents.

👀 Humans experience a continuous stream of input and reasoning.

We build tiny hypotheses along the way:

“Let me hover over the tooltip to see what this button is for.”

It’s a loop of sense → reason → act, in continuity.

🧠 Agents, on the other hand, live in snapshots:

See screen → Decide → Act → See new screen.

They’re like a human who:

• Looks at the screen
• Writes a letter to a controller to perform an action
• Closes their eyes while it’s happening ← VERY IMPORTANT
• Opens their eyes to a new scene – with no memory of the past The only continuity? 📝

A notepad on the table – a few scribbled notes before they "blacked out".

So we asked ourselves:

“If this were me, how would I use that notepad?”

We’d been giving agents summaries of prior steps – but something was still missing.

So we made a small tweak to the prompt:

👉 “Write a note to your future self”

Result: the agent now jots down whatever it wants its future self to know, such as:

• What hypothesis it’s testing
• Why it chose this action
• What to look for in the new state

So in the next iteration when it wakes up, it knows: “What was I thinking?”

That single line — “Write a note to your future self” —

gave our agent a memory-like thread.

A small change. A big leap in clarity and navigation. 🚀

#AI #Agents #LLM #StartUp #BuildInPublic #AgenticAI