Software Testing - Performance Testing

1. What is Performance Testing?

Performance Testing is a type of testing that evaluates how well a system performs under specific workload conditions.
It checks the speed, scalability, stability, and reliability of an application.

In simple words:

Performance Testing ensures your website/app works fast, stays stable, and performs well even when many users are using it.

2. Goals of Performance Testing

Performance testing is not about finding functional bugs — it focuses on system behavior.

✔ Speed

Is the system fast enough? (response time)

✔ Stability

Does it remain stable under sustained load?

✔ Scalability

Can the system grow and handle increasing users?

✔ Resource Utilization

Does it use CPU, memory, and network efficiently?

✔ Identify Performance Bottlenecks

Slow API endpoints, DB queries, server overload issues, caching issues, etc.

3. Why Do We Perform Performance Testing?

  • To ensure fast response times

  • To verify reliability during peak traffic

  • To avoid crashes during high user load

  • To validate infrastructure capacity and configuration

  • To detect bottlenecks before users do

  • To improve user experience (UX) and SEO

  • To ensure the system is ready for big events like marketing campaigns or launches

4. Types of Performance Testing (Very Important)

1) Load Testing

Tests system performance under expected user load.

Example: 1,000 users browsing the site.

2) Stress Testing

Tests the system beyond normal limits to find the breaking point.

Example: Increasing users until the server crashes.

3) Spike Testing

Sudden, extreme increase in load to check the system’s reaction.

Example: Traffic jumps from 100 to 5,000 users instantly.

4) Endurance (Soak) Testing

Check how the system performs under continuous load for a long time.

Example: 200 users for 12 hours → detect memory leaks.

5) Scalability Testing

Tests how well the system scales when adding more resources (servers, CPUs, RAM).

6) Volume Testing

Check system performance with large amounts of data.

Example: Database with millions of records.

Summary Table

Test Type Purpose
Load Expected load
Stress Beyond limit / breaking point
Spike Sudden load increase
Endurance Long duration testing
Scalability Ability to scale
Volume Handle huge data

5. Performance Testing Metrics

Important performance metrics include:

Speed Metrics

  • Response Time

  • Page Load Time

  • Latency

Capacity Metrics

  • Throughput (requests/sec)

  • Concurrent Users

Reliability Metrics

  • Error Rate

  • Failure Rate

  • Timeout Responses

Resource Metrics

  • CPU Usage

  • Memory Usage

  • Disk I/O

  • Network Bandwidth

  • DB Query Time

6. Performance Testing Process (Step-by-Step)

Step 1 — Gather requirements

Expected users? Expected response times?

Step 2 — Identify key scenarios

Examples:

  • Login

  • Search

  • Add to Cart

  • Checkout

  • API hits

Step 3 — Define KPIs and test data

KPIs like:

  • Response time < 3 seconds

  • 500 users concurrent

Step 4 — Choose tools

(Examples below)

Step 5 — Prepare test environment

Use a production-like setup.

Step 6 — Execute tests

Load test, stress test, endurance test, etc.

Step 7 — Monitor system

Check logs, servers, DB performance.

Step 8 — Analyze results

Find bottlenecks: slow APIs, DB locking, CPU spikes.

Step 9 — Optimize

Developers and DevOps fix issues.

Step 10 — Retest

Repeat testing after improvements.

7. Popular Performance Testing Tools

Open-Source Tools

  • JMeter

  • Locust

  • Gatling

  • k6

  • Tsung

Enterprise/Cloud Tools

  • LoadRunner

  • BlazeMeter

  • Neoload

  • AWS Performance Testing Tools

  • Azure Load Testing

8. Example of Performance Testing

Scenario: E-commerce Application

Expected load:

  • 800 concurrent users

  • Response time < 2 seconds

Actual test results:

  • Response time: 3.2 seconds → Slow

  • Throughput: 350 req/sec → OK

  • CPU: 95% → Too high

  • DB query: 600ms → Slow

Outcome:
Performance optimization required (caching, indexing, code refactoring).

9. Common Mistakes in Performance Testing

  • Testing on weak environments (not production-like)

  • No clear KPIs

  • Using unrealistic user behavior

  • Ignoring database performance

  • Not monitoring server/resource usage

  • Not retesting after fixes

  • Poor test data preparation

10. Best Practices

  • Always test in a staging environment similar to production

  • Use realistic scenarios and user data

  • Monitor client-side and server-side together

  • Run different types of performance tests (load, stress, endurance)

  • Identify and document bottlenecks clearly

  • Involve developers and DevOps in analysis

  • Retest after optimizations