Distributed Trace Analysis: Use a trace to locate cross-service latency and error propagation
Follow a user request across services, connect trace spans into a causal path, and use span timing to locate latency and evidence gaps.
#AI Testing
Log Analysis: Cluster errors at scale and locate post-release issues
Cluster logs by structured fields, request IDs, and error patterns. Keep query windows and samples; state redaction, sampling, timezone, and data gaps.
Metrics Anomaly Analysis: Use anomalous metrics to locate the time window, impact, and candidate causes
Validate metric spikes against historical baselines, correlated indicators, seasonality, and user impact before escalating an alert as an incident.
Capacity Planning Analysis: Use business growth and resource curves to judge remaining system capacity
Translate demand forecasts and saturation metrics into headroom, growth scenarios, and failure thresholds to plan when systems need more capacity.
Performance Bottleneck Analysis: Combine load tests, metrics, and traces to find the real limiting resource
Use traces, saturation signals, and controlled comparisons to follow request paths and find the first resource limiting throughput or amplifying latency.
Performance Regression Analysis: Compare versions to determine whether performance really regressed
Compare versions on a stable baseline and environment, quantify uncertainty, and link performance shifts to changed behavior before optimizing.
Performance Result Analysis: Read load-test results from throughput and tail latency to errors and resource use
Correlate throughput, tail latency, errors, saturation, and workload shifts to locate bottlenecks and support release or scaling decisions.
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.
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.