Jas Manigundan
Jas Manigundan
Lead Automation Engineer, Culture Amp
I am a lead test automation engineer working as a part of the QE practice at Culture Amp led by the legendary Anne-Marie Charret.
I'm a developer turned tester. Even though writing code to build applications is so much fun, my now best friend showed me a few years ago that helping write good quality apps is much more fun. I jumped into testing from being a developer and have never looked back. I'm very passionate about native testing frameworks for web, backend, infra and mobile platforms
CI/CD is something I am very passionate about. On the web side of things, AWS CDK has been quite intriguing. I have been an advocate of Infra as Code since working on it. Devops is something that is quite exciting for me, especially given it is always evolving.
I have been speaking at a few international conferences on testing and CI/CD. Saucecon'21, Ministry of Testing's Testbash and UI testing week events to name a few.
It has been so much fun building testing frameworks for a wide range of platforms including mobile, web, infrastructure and microservices.
Please feel free to reach out to chat anything testing.
🎂 MoTaBirthday | March 17, 2022
Open To
Write
Teach
Speak
Mentor
Jaswanth Manigundan is running a fast paced session at Testbash where he speaks about the lower level tests that one can write and discusses with a very fun group of people.
20 Jan
Why is it important to keep mobile tests inside the native development framework?
23 Nov
Explore how AI agents and Playwright MCP act as testing helpers that speed up web testing while you stay in control
12 Dec
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(1)
Goldens for your evals
Hi Swathika, sharing some context from my experience over the last year. Hope it helps.
- Evaluation Scenarios - Start small with a "golden dataset" (1–3 examples per scenario). It aligns the team on "what good looks like" without becoming a maintenance burden.
- Shifting Left: Integrate test-case preparation directly into user stories and acceptance criteria. Leaving this until after launch is incredibly painful and doesn't scale. The whole team should own this and contribute.
- Golden data as part of Requirements - 100% yes. In an indeterministic LLM world, building before defining your expected responses makes no sense.
- Metrics (50 scenarios / 80%) - Numbers alone are meaningless. An 80% score is a failure if the missing 20% covers your core features. Assign weightage to critical scenarios, start small, and iterate. It depends on the domain you are in - for example, bias might have more weightage than another factor for certain industries like HR.
Not registered for any upcoming events
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