Tag: Testing
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Building the AI Engineering Stack: Spec-Driven Development Takes Center Stage
The Weekly Radar The Context AI coding agents have evolved from simple autocomplete to complex ecosystems capable of planning features, modifying entire repositories, executing tests, and coordinating multi-agent workflows. In response, a set of disciplines—Spec-Driven Development, context engineering, specification engineering, agentic engineering, and harness engineering—has emerged to tame these autonomous tools. A “harness” determines what…
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Generative AI Agents Are Reshaping the SDLC—from Code Generation to Continuous Testing
The Weekly Radar The Context Over the last week Thunders’ analysis highlighted a real shift: embedding agentic AI test frameworks into the SDLC to generate, execute, and monitor tests automatically. Instead of hand-crafting test scripts, teams can deploy AI agents that discover edge cases, maintain coverage, and adapt to code changes on the fly. This approach can cut manual QA effort by up to 40% and tighten the…
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Agentic SDLC: Embedding AI Agents Across the Development Lifecycle
The Weekly Radar The Context The Agentic Development Lifecycle (ADLC) reframes the traditional six-stage SDLC—Plan, Design, Build, Test, Deploy, Maintain—into a closed loop powered by AI agents. In this model, an LLM like Claude captures intent directly (writing requirements as version-controlled intent.md), consolidates design and spec phases into a single interactive session, and then automates code and test generation together, surfacing institutional knowledge via CLAUDE.md rather than siloed docs. At each handoff, continuous AI-driven evaluations…
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Integrating AI Across the Software Development Lifecycle
The Weekly Radar The Context AI coding tools have moved far beyond simple autocomplete. Today’s best-in-class solutions can plan features, refactor code, review pull requests, identify security issues, debug production problems, and maintain documentation—all within the same workflow. This shift means AI is not replacing individual tasks but becoming an integrated collaborator across the entire software development lifecycle. Developers who benefit…
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Mutation Testing Adoption—Elevating Test Quality Beyond Coverage
Weekly Radar The Context Over the past 2–3 years, mutation testing has shifted from academic curiosity to an enterprise practice. Unlike traditional code coverage—which merely tracks what lines are executed—mutation testing introduces small code changes (“mutants”) and verifies whether the existing test suite catches them. Leading frameworks (e.g., Stryker, Pitest) now integrate with CI pipelines…
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AI-Powered Contract Testing—From Hype to Hard ROI
THE WEEKLY RADAR The Context Over the past week, we’ve seen rapid evolution in contract testing frameworks leveraging AI to generate, update, and validate API contracts. Solutions like TestSprite’s AI-native engine and enhancements in Pact are promising fully automated workflows—from contract creation to provider verification—without human intervention. This innovation is framed as the next frontier in microservices…
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Spring Boot Application with Jersey Rest End Points
Yesterday I received a requirement to create a JAVA Spring Boot Application with Jersey Rest end-points. Researching on Internet about how to overwrite the SB configuration I founded that it solves with only one dependency! So, here is a little example about how to configure and test (with Mockito and EasyMock) a Jersey end-point in…