{
  "id": 2612367,
  "title": "AI Is Changing What 'Correct Software' Means",
  "url": "https://urgent.news/2026/08/22/ai-is-changing-what-correct-software-means",
  "topic": "ai",
  "section": "AI",
  "published": "2026-08-22T16:41:58.000Z",
  "source": {
    "name": "Dev.to",
    "slug": "dev-to",
    "url": "https://dev.to/majdizlitni/ai-is-changing-what-correct-software-means-14hf"
  },
  "original_language": "en",
  "account": "In the realm of software engineering, the concept of correct software has evolved. Traditionally, correctness was defined by deterministic assertions, where expected outputs were verified through thorough testing. However, with the advent of AI-generated code, this simplistic approach is no longer sufficient. AI-generated code can appear syntactically perfect and test-compliant, yet it may be semantically incorrect, violating non-functional system boundaries in ways that are only apparent in production environments.\n\nThe determinant of correctness in software engineering is no longer limited to writing code – it has shifted towards verifying its execution intent. This shift is crucial as AI-generated code poses new challenges to traditional testing methods, particularly unit tests, which often fail to catch AI era bugs.\n\nOne of the major issues with AI-generated code is that it can follow patterns based on trained context and user prompts, leading to code that passes all unit tests but violates critical system boundaries. For instance, it might add hidden performance costs, break state machine idempotency under concurrent execution, or bypass domain entity validation rules by instantiating raw records directly. Consequently, the code appears perfectly valid, but it is architecturally incorrect.\n\nTo address these challenges, a new approach is required. Instead of relying solely on unit tests, software engineering must shift towards defining correctness by system invariants, which are enforced at runtime. This approach defines correctness in terms of bounded runtime guardrails and architectural fitness functions, rather than just checking if a function returns an expected output.\n\nPractically, this involves implementing invariant verification in the software architecture pipeline. This includes enforcing system invariants before code merge (during CI) and during runtime using verification engines. These engines continuously check structural constraints across the entire codebase execution model, ensuring that the system design remains valid.\n\nFor example, using .NET and Azure, one can enforce that AI-generated code cannot introduce mutable state into the domain layer, and that every command handler implements idempotency decorators. Additionally, a runtime semantic guardrail verification engine can be implemented to validate the intent of AI agents before allowing any code or data changes. This engine uses prompts to define hard system invariants that the AI engine cannot negotiate, ensuring that no direct database access occurs outside of repositories, no performance-heavy allocations are made, and every external change triggers events using Microsoft.SemanticKernel.",
  "summary": "Software engineers used to define a \"Correct software\" via a simple deterministic binary an Expected Output . All Test are passed, but this is collapsing, a green CI pipeline no longer guarantees correct software, with AI-generated code that's look syntactically perfect and test-compliant yet they are semantically wrong in a ways that appears in productions. The bottleneck in software engineering…",
  "key_points": [],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}