Systems | Development | Analytics | API | Testing

dreamfactory

OpenAPI Schema Validation for AI

Apr 24, 2026 By Kevin Hood In DreamFactory
Schema validation ensures AI agents interact with APIs accurately by enforcing strict rules for requests and responses. OpenAPI provides a clear, machine-readable contract for APIs, reducing errors and improving reliability. This approach eliminates issues like ambiguous responses or schema drift, ensuring predictable behavior and secure data access.
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keploy

Software Release Life Cycle: Stages, Process, And Best Practices

Apr 24, 2026 By Keploy In Keploy
The software release life cycle (SRLC) is where most engineering failures begin. Not because of bad code, but because of a broken release process. In modern environments, applications run across APIs, microservices, and cloud infrastructure, where even small changes can ripple far. A well-defined release cycle – with clear stages, automated validation checkpoints, and rollback strategies is what gets code to users without surprises. Traditional testing validates components in isolation.
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The Claude Bill is Too Damn High #speedscale #claude #aiagents #aicoding #devops #llms

Apr 24, 2026 By Speedscale In Speedscale
Stop overpaying for AI reasoning by trading expensive GPU cycles for efficient, deterministic testing. This video explores how tools like linters and traffic replay can complement Claude, helping you fix bugs more accurately while cutting token usage by up to 50%. Visit: speedscale.com to learn more.
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keploy

In-Depth Testing: Stop Shipping Bugs Your Tests Missed

Apr 24, 2026 By Keploy In Keploy
I’ve pushed code that cleared every CI check, watched the green badge appear, shipped to production — and then spent the next two hours on a rollback. That experience was my real introduction to in-depth testing. In-depth testing is the practice of validating software behavior across multiple layers: unit logic, component interactions, end-to-end user flows, and failure conditions.
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smartbear

Git review for TestComplete projects

Apr 23, 2026 By Temil Sanchez In SmartBear
Teams using TestComplete face a common problem: one small test change can produce a wide set of modified files, and not all of them deserve the same level of scrutiny. The fix is not to review everything equally – it is to classify TestComplete artifacts by risk, then standardize how your team reviews, stages, and merges them. This article outlines this process and offers best practices for using Git effectively with TestComplete projects.
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dreamfactory

DreamFactory 7.5.0 Release: GitHub-Connected AI Agents, a Platform-Wide Security Hardening Pass, and a Smoother MCP Authoring Experience

Apr 23, 2026 By Kevin McGahey In DreamFactory
DreamFactory 7.5.0 is focused on two audiences that have been growing fastest in our user base: teams wiring LLM agents to production databases through MCP, and security and platform teams hardening those deployments for real-world traffic.
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kong

Introducing Kong A2A and MCP Metrics: Visibility and Governance for AI Tool Adoption at Scale

Apr 23, 2026 By Amit Shah In Kong
Scaling LLM and agentic AI adoption from pilot programs to enterprise-wide deployments is a massive logistical rollout. As AI and agentic usage grow, so does a nagging question for leadership: **Are agents using the right tools to get the job done?** While raw infrastructure metrics might tell you if a server is "up," they fail to tell you if your AI investment is being leveraged.
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loadfocus

10 Ways to Optimize API Performance Testing for Faster, More Reliable Results (2026 Guide)

Apr 23, 2026 By LoadFocus In LoadFocus
Many teams dedicate time and resources to API performance testing, yet still face sluggish releases and delayed deployments. Incidents slip through, and users encounter slow applications. The root cause? Too often, teams treat performance testing as a checkbox, without truly simulating real-world loads or analyzing key performance metrics such as latency, throughput, and error rates. This leads to a false sense of readiness that quickly unravels in production environments.
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Why the "tsunami of code" is breaking QA | From the Bear Cave Ep. 3

Apr 23, 2026 By SmartBear In SmartBear
Recent SmartBear research shows that 70% of teams are already seeing quality degrade with AI-generated code, creating a real bottleneck in the software-development lifecycle (SDLC). As output increases, QA teams are left choosing between delaying releases to validate changes or shipping faster with less confidence in what’s actually working. In this From the Bear Cave clip, SmartBear CEO Dan Faulkner and CMO Kelly Wenzel dig into a growing gap in modern software development: how AI is accelerating code generation but testing and quality validation aren’t scaling with it.
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Velocity can't come at the cost of quality

Apr 23, 2026 By SmartBear In SmartBear
AI-generated code is flooding your pipelines. Your test automation debt is piling up. If this sounds familiar, you're not alone. Velocity can't come at the cost of quality. As AI transforms how we build software, API testing must evolve. Join Justin Collier, Senior Director, Product Management, and Yousaf Nabi, Developer Advocate, to explore the future of API testing in an AI-driven world.
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