Performance Testing Interview Questions and Answers (2025) | JaganInfo

Performance testing evaluates how a system performs under expected workload focusing on responsiveness, stability, and resource usage.

Load testing, stress testing, endurance testing, spike testing, and scalability testing.

Load testing measures system behavior under expected user or transaction load to ensure it meets performance goals.

Stress testing evaluates system stability by subjecting it to loads beyond normal operational capacity to assess failure points.

Endurance testing checks system performance over an extended period to detect issues like memory leaks or degradation.

Response time, throughput, resource utilization (CPU, memory, disk I/O), and error rates.

JMeter, LoadRunner, Gatling, NeoLoad, Apache Benchmark, and Locust.

By identifying bottlenecks and performance issues, allowing optimization to reduce latency and improve responsiveness.

A baseline is the benchmark performance data collected from tests to compare against future test results.

Performance test plan, test scenarios, scripts, test reports, and defect logs.

Identify objectives, scope, test environment, workload models, metrics collected, schedule, and roles.

Use workload models based on real user behavior, concurrent users, think times, and transaction mixes to realistically mimic user load.

Ramp-up time is the period over which virtual users are gradually started to avoid sudden load spikes.

Throughput is measured as the number of transactions processed per second or requests handled per unit time.

Think time simulates the actual delay between user actions, reflecting realistic user behavior.

Analyze metrics for high response times, CPU/memory saturation, thread contention, and database slow queries.

Load testing checks behavior at expected peak load (e.g., 1000 concurrent users), while stress testing pushes beyond capacity (e.g., 1500 users) to observe failure.

Correlation captures dynamic server responses (like session IDs) and reuses them in subsequent requests to maintain valid sessions.

Compare metrics with requirements, verify system logs, monitor resource usage, and confirm absence of errors.

Spike testing abruptly increases load to check if the system can handle sudden traffic surges and recover gracefully.

Scalability testing evaluates the ability of a system to scale up or out to meet increasing load without performance degradation.

Run tests over extended duration with a typical load to detect memory leaks, resource depletion, or performance degradation.

Client-side testing measures response time and rendering in user’s browser/device; server-side testing focuses on backend processing and resource utilization.

Code optimization, caching, connection pooling, database query tuning, load balancing, and efficient resource management.

Using tools like top, vmstat, perfmon, Grafana, Prometheus, and application-specific monitoring for CPU, memory, disk, and network.

Correlation handles dynamic session data to prevent errors; parameterization simulates real user inputs to provide varied test data.

By analyzing metrics across components, profiling applications, tracing transactions end-to-end, examining logs, and using monitoring tools to locate CPU, memory, disk I/O, or network hotspots.

Capacity planning estimates the required resources and infrastructure to meet future load demands while maintaining performance and availability.

Network latency adds delay affecting response times; mitigation includes using network emulators for testing, optimizing data size, caching, and deploying edge computing.

By simulating concurrent API calls, measuring response times, throughput, error rates, and analyzing server resource consumption using tools like Postman, JMeter, or Gatling.

Testing query execution times, indexing strategies, transaction throughput, connection pooling, and scalability under increased load using tools like HammerDB or JMeter.

Garbage collection causes periodic CPU pauses which can increase response times; tuning GC parameters and using concurrent collectors mitigates impact.

Distributed tracing tools like Jaeger or Zipkin track requests across microservices to identify latency sources and bottlenecks.

Throughput is volume of completed transactions per time; latency is time to complete a single transaction. Interpretation involves trade-offs and bottleneck identification.

By modeling think times, session handling, pacing, varied input data, and differing usage patterns reflective of actual users.

Containers allow consistent environments for tests; cloud platforms enable scalable test environments and distributed load generation.

Integrate performance scripts into build pipelines, trigger tests on commits or releases, analyze results automatically, and enforce thresholds for promotion.

Optimize data partitioning, caching, reduce shuffles, tune memory, and select appropriate file formats and compression.

Device fragmentation, network variability, power consumption, UI responsiveness, and backend integration complexities.

By monitoring memory use over time, analyzing heap dumps, and using profiling tools.

Proper indexing improves query speeds but can slow down write operations and increase storage requirements.

Caching reduces load on backend systems and speeds up response times, helping identify if performance issues are cache-related.

By generating high traffic volume beyond expected norms to test system resilience and mitigation mechanisms.

Using APM tools like New Relic, Dynatrace, or AppDynamics to track performance metrics, detect anomalies, and alert teams in real-time.

Tune GC algorithms, heap sizes, use concurrent collectors, and minimize object allocations.