How Non‑Coding Test Engineers Can Master Performance Testing Without a Technical Barrier

1. Choose No‑Code‑Friendly Tools

Select visual, click‑based tools that satisfy most business scenarios without requiring scripts.

1.1 Interface performance testing with Postman

Step 1: Prepare interface information – Record the target API (e.g., payment URL, POST method, parameters "amount=100&userId=123") in a Postman collection.

Step 2: Set concurrency parameters – Open the Runner, choose the collection, set the iteration count (e.g., 1000 requests) and delay (e.g., 100 ms between requests) to simulate continuous user actions.

Step 3: Review results – After execution, Postman generates a report showing failure rate and average response time; a failure rate above 5 % or an average response time exceeding the target indicates a performance issue.

Example: testing an "add‑to‑cart" API with 1000 iterations and 100 ms delay yields a 12 % failure rate and a 3 s average response time, signalling the API cannot handle high concurrency.

1.2 Page performance testing with Lighthouse

Step 1: Open the page – Load the target page (e.g., an e‑commerce homepage) and open Chrome DevTools, then switch to the Lighthouse tab.

Step 2: Select the Performance audit – Uncheck other categories (SEO, Accessibility) and click “Generate report”.

Step 3: Examine key metrics – Focus on First Contentful Paint (≤1.8 s), Largest Contentful Paint (≤2.5 s), and Time to Interactive (≤3.8 s). The report also highlights causes such as oversized images.

Example: Lighthouse reports a Largest Contentful Paint of 4.2 s for an app’s H5 page and flags an uncompressed 2 MB banner image, indicating the need to compress images.

1.3 Server performance monitoring with Prometheus + Grafana

Open the pre‑configured Grafana dashboard, select the relevant server node, and observe real‑time metrics: CPU usage (warning above 80 %), memory usage (warning above 90 %), and network bandwidth.

Example: during a 100‑user concurrent login test, CPU spikes to 92 % and memory to 85 %, suggesting the server requires scaling.

2. Clarify the “3‑Step Test Process”

2.1 Step 1 – Break down requirements

Ask two questions to turn vague demands into concrete indicators: “Which scenario to test?” (e.g., split “APP performance” into launch time, homepage load, cart concurrency) and “What is the acceptance criterion?” (e.g., homepage load ≤3 s, failure rate ≤5 %).

Example: an activity page must support 1000 simultaneous users, load ≤3 s, failure rate ≤5 %, and CPU usage ≤80 %.

2.2 Step 2 – Design realistic scenarios

Reproduce the actual user flow without complex scripting. For an activity page, the scenario could be: open the app → navigate to the activity page → click “claim coupon” → close the page, repeated 1000 times to simulate 1000 users.

Use Postman or Lighthouse to configure these steps; no code is needed, only parameter settings that mirror the user path.

2.3 Step 3 – Analyze results with business‑oriented metrics

Focus on three categories of metrics that stakeholders can understand:

User‑experience metrics : app launch time, page load time, button response time (issues if >3 s).

Functional stability metrics : API failure rate, page crash count (e.g., 5 crashes in 1000 visits = 0.5 % failure, acceptable if ≤1 %).

Server‑resource metrics : CPU, memory, bandwidth (viewed via Grafana or OS tools).

3. Problem Localization from a Business Perspective

3.1 Describe the symptom, not the code

When a page loads slowly, state the scenario, observed value, and impact (e.g., “100 users see a 5 s load time, exceeding the 3 s target, which may cause abandonment”).

3.2 Provide data‑backed evidence

Attach tool‑generated reports such as a Lighthouse screenshot showing a 5 s Largest Contentful Paint or a Postman report with a 12 % failure rate, allowing developers to pinpoint causes (e.g., oversized images).

3.3 Verify after fixes

After developers address the issue, rerun the original scenario to confirm that load time drops below 3 s and failure rate falls under 5 %.

4. Pitfalls to Avoid

Custom‑script scenarios (e.g., token generation and chained API calls) should be handed to colleagues familiar with JMeter or LoadRunner scripting.

Low‑level performance tuning (SQL optimization, server parameter tweaks) belongs to developers or operations engineers.

By choosing visual tools, following a structured process, and focusing on business‑relevant metrics, non‑coding test engineers can reliably perform performance testing and add measurable value to their projects.