Gatling Performance Testing: Turn real workload models into executable load-test scripts
Build Gatling tests with Scala scenarios, feeders, request chains, and injection profiles that reflect real user journeys and stay tied to business goals.
#AI Testing
JMeter Performance Testing: Turn real workload models into executable load-test scripts
Make JMeter plans reproducible with explicit thread groups, CSV test data, assertions, and result capture for review, handoff, and CI.
k6 Performance Testing: Turn real workload models into executable load-test scripts
Build k6 scenarios from a workload hypothesis, modeling user pacing, arrival rates, thresholds, and telemetry so results can guide capacity decisions.
Performance Testing: Turn “the system must be fast” into executable, verifiable goals
Set performance goals, model expected user load, and measure tail latency and transaction experience to test service boundaries against user expectations.
Performance Workload Modeling: Build realistic workload models from production traffic and user behavior
Translate production traffic and user journeys into concurrency, arrival rates, pacing, and data variation to build a realistic, repeatable load test.
Flaky Test Analysis: Why do flaky tests fail intermittently?
Classify intermittent failures by timing, data, order, environment and product behavior before deciding to repair, quarantine or monitor.
Regression Scope Analysis: Define regression boundaries from changes, dependencies, and past defects
Set regression scope from change evidence, dependency paths and risk; state which behaviors are covered, deferred or still exposed.
Regression Test Selection: Find the smallest sufficient regression suite
Choose regression tests by affected contracts, user impact and defect history, preserving fast feedback while recording uncovered risk.
Change Impact Analysis: Trace service, data, test, and release impact from a code change
Trace code changes through behavior, dependencies, data and consumers to identify tests that need fresh evidence and risks that remain.
Code Review: Look beyond code changes to behavior and risk
Review test code for assertion quality, isolation, determinism, and boundary risks so a green result reflects reliable behavior.
PR Test Impact Analysis: Quickly locate affected test scope from code changes
Trace changed files through dependencies and call paths to select risk-based regression checks that cover affected behavior, not just directories.
REST Assured API Automation: Turn API definitions into sustainable CI regression
Keep Java API requests, contract assertions, and failure diagnostics readable with REST Assured so reviews can see intent and evidence.