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Are you testing distributed behavior – or just hammering endpoints?

Validate whole workflows under load — not just individual requests.
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Can your test suite catch what breaks between services – not just at the surface?

A 200 response confirms the API accepted the call — nothing more. Whether the message was consumed, the record written, or concurrent users corrupted shared state is a different question entirely.

Here’s how QALIPSIS helps QA engineers overcome the most critical testing challenges.

Why does your test suite stay green while the queue falls behind?

  • The API accepts the request and returns 200. Meanwhile the message broker queue is backing up — the consumer service cannot keep pace under peak load. No test is watching the downstream side, so the queue keeps growing, processing falls further behind, and by the time someone notices, a large backlog has accumulated. The test suite showed green throughout.
  • QALIPSIS models the message path as steps inside the scenario. The API call triggers first; then the Kafka or RabbitMQ plugin consumes the downstream message and a join operator correlates the original request with its observed outcome. If the message never arrives — or arrives outside the expected window — the assertion fails at the exact step, with an event recording when and why.
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Which shared resource breaks when a thousand users write at the same time?

  • The platform works correctly with one user. With hundreds of simultaneous writes to the same resource — inventory decrements, reservation commits, profile updates — silent data corruption appears. Race conditions only surface under concurrent load, and sequential test execution never reproduces them. They remain invisible until a promotional event brings them out in production against real customer data.
  • QALIPSIS runs multiple minions through the same write path simultaneously under a realistic load profile. After each stage, the database plugin verifies that persisted records reflect the expected state — no duplicates, no dropped writes, no last-writer-wins overwrites. Join operators match each submitted request against its stored outcome, making divergences visible at the step level rather than in a post-incident audit.
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Which step in the chain is silently eating your latency budget?

  • Each individual service looks fast in isolation. But the user experience depends on a chain — authentication, entitlement check, content lookup, session creation — and the SLO applies to the whole sequence, not any single call. Without a scenario that exercises the chain under load, test coverage is a collection of unit benchmarks that says nothing about actual user experience at peak.
  • QALIPSIS models the full journey as an ordered sequence of HTTP steps, reusing the same connection across steps within a single minion’s execution. Each step is instrumented with meters, and verify steps assert that time-to-last-byte stays within the SLO budget. Exported per-step latency distributions make it clear which step in the chain degrades first as load increases.
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Did the last regression ship because the error rate stayed at zero?

  • Load tests run in CI, the error rate stays at zero, and the build passes. A service degraded to ten times its normal latency on every request throughout the campaign, every call eventually resolved, and nothing failed the build. The regression shipped. Error rate is not a proxy for latency correctness or functional accuracy — it just means the server responded.
  • QALIPSIS integrates into the build via the Gradle plugin. Assertion thresholds are defined per step — on latency percentiles, error rates, and functional outcome verification — and JUnit-style reports are exported for the pipeline to consume. Any breach fails the build with a report identifying the step, the failure type, and the violated assertion, giving the team an actionable signal rather than a binary exit code.
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Why are your test results stuck inside the test tool?

  • Test results live inside the test tool. The team’s dashboards run in Grafana over InfluxDB, or Kibana over Elasticsearch. After every campaign, someone manually extracts metrics or screenshots charts. Comparing performance across releases is guesswork, and overlaying test signals with infrastructure metrics to find correlations is simply not possible in that workflow.
  • QALIPSIS exports step-level events and meters to the backends your team already operates — Graphite, Elasticsearch, Kafka, TimescaleDB/PostgreSQL, or InfluxDB. Every data point is tagged by campaign, scenario, step, and zone, making it possible to filter by test run, track regressions across releases, and correlate load test signals with infrastructure behaviour in the same dashboards your team uses every day.
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Performance testing made for QA engineers

With QALIPSIS, you can:

Test event-driven, async architectures with ease

Scale load tests on demand

Simulate complete user journeys

Automate validation in every release cycle

What you need to know

Confident testing. Faster releases. Better software.

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