Clear Technical Narrator
deepu varmaによるHello everyone.
We are the IERP Testing Team, and our motto is very simple:
Keep Learning and Keep Improving in Testing.
With this mindset, our responsibility doesn’t stop when a release goes live. Once a release goes live and enters the hyper‑care period, that’s when production starts giving us real feedback — and as testers, that feedback is extremely valuable.
Because production has a habit of reminding us that testing is never really over.
Let me share a real example.
During our S/4 release, once we went live, we received a huge number of ServiceNow incidents. In total, we had around 400 incidents. To analyze them, we had to:
Go into each individual area
Download incidents manually
Check them one by one from a tester’s lens
Many of the incidents were similar or duplicates, but since everything was manual, we still had to go through each one. This took much more time than we expected — not just for analysis, but also to prepare lessons learned for the release.
That struggle is where my idea HyperWatch was born.
With HyperWatch, we decided to leverage IBM BOB AI to make this smarter and faster. BOB AI goes into all project areas, downloads all incidents, removes duplicate and similar ones, and provides us with a filtered, clean dataset.
Once AI does that heavy lifting, QA takes over.
We analyze:
Which area was impacted the most
What type of issue occurred
Why it failed
Whether it was a data issue, environment issue, configuration issue, or a testing gap
Based on this, we enhance test cases, improve regression coverage, and make sure that from the next releases onward, these issues don’t come back.
The outcome is clear:
Reduced production issues
Faster analysis
Stronger test coverage
More confidence in future releases
So instead of asking,
“Why did this happen in production?”
we confidently say,
“We already covered this in testing.”
That’s HyperWatch —
turning hyper‑care production issues into stronger testing and safer future releases.
Thank you.
