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All digests tagged Software Testing
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Building an AI code review agent requires more than a simple sandbox setup. Key fixes include model inversion, deep codebase context, autonomous testing, and cost-efficient model routing.
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The future of AI agents requires 'babysitters.' We discuss moving from simple API calls to auditable, intent-based workflows using Jev and WebMCP, and how to verify business outcomes with tools like Cucumber.
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Most agent evaluations are easy to misread. To test AI agents like a scientist, you must design realistic tasks, run repeated tests, and focus on full traces and observability, because production is the final truth.
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The speaker argues that passing tests do not guarantee user safety, especially in AI/ML systems. He proposes shifting focus from 'coverage' to 'impact' by documenting edge cases and defining 'must never' invariants.
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Comparing agentic search vs. vector search for coding agents, this talk reveals that while both methods achieved equal accuracy, vector search cost four times more due to lack of connective context.
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Coding agents are great at generating code, but verification is the hard part. Learn how to use test oracles, anti test slop, and ADRs to ensure AI-generated code is reliable.
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Discover Android Studio Quail 2 stable updates, including parallel agentic chats and enhanced Leak Canary. Learn about Android Bench's shift to the Harbor framework for LLM evaluation and details on the $2M Build with Gemini XPRIZE.
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Building large systems with AI requires more than 100% test coverage. Observability and using existing 'test articles' (like S3) are key to enforcing correctness in distributed environments.
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Discover advanced .NET testing techniques—from Property-Based Testing to Mutation Scoring—to write fewer, but far more effective tests and build true confidence in your code.
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