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keploy

API Regression Testing: Catch Breaking Changes Early

Oct 27, 2026 By Keploy In Keploy
API regression testing re-runs your API tests after every code change so existing endpoints keep returning what clients expect. A solid setup checks response shapes, auth, and latency, mocks external dependencies, and runs a fast suite on each pull request with the full suite nightly. Keploy, an open-source tool, generates that suite and its dependency mocks from real API traffic, so teams get regression coverage without writing scripts.
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keploy

Regression Testing vs Integration Testing: Key Differences

Oct 27, 2026 By Keploy In Keploy
Here is a situation most backend teams have lived through. A developer changes how the user service formats dates. It is a tiny change: one line, a cleaner ISO format, every unit test still green. The pull request gets approved in five minutes. Two days later, the billing service starts failing to generate invoices, because it was still expecting the old date format from the user service. Two kinds of testing could have caught that, and they are the subject of this post.
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Why AI Coding Agents Are Breaking Your Software

Oct 27, 2026 By Speedscale In Speedscale
While AI coding agents increase initial build speed, they often lack context about real-world user behavior, leading to downstream production bugs. Watch to learn how to move beyond basic agent-on-agent reviews and set up a true "Quality Factory" using: Check out our demos or get started free: speedscale.com.
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speedscale

gRPC Mocks, Tests and Contract Checks From a .proto

Oct 26, 2026 By Matt LeRay In Speedscale
Change one field in a.proto from double to string, and nothing complains. The code compiles, the deploy goes green, and a service still built from the old contract keeps answering with a double. Here’s what Go’s protobuf runtime does when a client on the new contract reads that answer: No error. risk_score comes back empty, and the nine bytes of the double are tucked away as unknown fields.
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speedscale

Inside Kraken: Regression Testing With Traffic Replay

Oct 26, 2026 By Matt LeRay In Speedscale
A readiness check should make a release safer. During one release validation, ours kept Kraken’s traffic replays stuck in Initializing until they timed out. The operator was asking Kubernetes for EndpointSlices. Its installed permissions did not allow that request. The code needed a real cluster, the generated permissions, and a replay trying to start before that mistake became obvious. Kraken put those pieces together during release validation.
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speedscale

Test and Mock GraphQL APIs Without Trusting the 200

Oct 26, 2026 By Matt LeRay In Speedscale
Replay a GraphQL recording at the server that produced it, and every request comes back 200 OK. Here is one of those 200s, from a Session query whose recorded token has expired: A GraphQL server reports most failures inside a successful HTTP response, so any check that stops at the status code passes this request. And that is the second way GraphQL hides from URL-shaped tooling.
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speedscale

5 Questions to Ask Before the Next Cloud Outage

Oct 23, 2026 By Alan Mon In Speedscale
On October 20, a power failure in one of Google Cloud’s three London zones disrupted Revolut, Lloyds, HSBC and others for hours. One zone failed, errors spread region-wide, and Google warned customers they might not be able to create new workloads to fail over to. Most teams find out how resilient they are during an outage, not before one. These five questions let you find out first.
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Test AI-Generated Code with Real Traffic#aiagents #devops #observability #shiftleft #softwaretesting

Oct 23, 2026 By Speedscale In Speedscale
AI can generate code in seconds, but testing it against real production behavior is where developers get stuck. Learn how capturing and replaying actual production traffic in development helps you catch bugs before you deploy!
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How to test Jira tickets automatically with an AI QA agent | SmartBear BearQ for Jira

Oct 23, 2026 By SmartBear In SmartBear
You can add AI-driven testing to your Jira workflow by handing tickets to the QA Lead Agent in SmartBear BearQ for Jira. Move a finished ticket, and the agent checks for an existing test, links or creates one, runs it, and reports back. If your team builds with coding agents like Claude or Cursor in Jira, BearQ, the agentic QA system, adds the quality step to that AI-driven software development lifecycle (SDLC).
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Kong Context Mesh

Oct 22, 2026 By Kong In Kong
Kong Context Mesh composes APIs, data, and documents into a single MCP interface that AI agents can call directly, with no custom harness or extra instructions. Built on Kong Konnect, it supports different authentication strategies and data structures, and avoids context window bloat through progressive disclosure and on-the-fly execution. Agents discover tools just in time and retrieve only the context needed for the task. The demo connects internal APIs and external MCP servers, then uses Claude Code to complete a multi-source task.
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