The threat extends beyond accidental errors. When AI writes the software, the attack surface shifts: an adversary who can poison training data or compromise the model’s API can inject subtle vulnerabilities into every system that AI touches. These are not hypothetical risks. Supply chain attacks are already among the most damaging in cybersecurity, and AI-generated code creates a new supply chain at a scale that did not previously exist. Traditional code review cannot reliably detect deliberately subtle vulnerabilities, and a determined adversary can study the test suite and plant bugs specifically designed to evade it. A formal specification is the defense: it defines what “correct” means independently of the AI that produced the code. When something breaks, you know exactly which assumption failed, and so does the auditor.
США впервые ударили по Ирану ракетой PrSM. Что о ней известно и почему ее назвали «уничтожителем» российских С-400?20:16
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Instead, you should treat Business-Modules as the units you want to test. A module has a clear, explicit API (public methods as well as required interfaces). It means that all the results and side effects can be observed via the API. So in tests you can apply stimulus to the module and assert the results/side effects - that should cover all possible paths (except for time based logic, but there are other methods to cover these cases).
。safew官方下载对此有专业解读
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