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AI and LLMs challenge software testing's deterministic assumptions

By PulseAugur Editorial · [1 sources] ·

Senior software engineer Mike Mannion discussed how Large Language Models (LLMs) are challenging traditional software testing methodologies. Speaking at a meetup in Bern, Mannion highlighted the shift from deterministic outcomes to probabilistic testing for AI systems. The discussion covered strategies for acceptable failure rates and building resilient AI testing frameworks. AI

IMPACT LLMs are forcing a reevaluation of core software testing principles, moving towards probabilistic approaches and acceptable failure rates.

RANK_REASON This is an opinion piece discussing the impact of LLMs on software testing, presented at a meetup.

Read on Mastodon — sigmoid.social →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

AI and LLMs challenge software testing's deterministic assumptions

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This is an opinion piece discussing the impact of LLMs on software testing, presented at a meetup.
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COVERAGE [1]

  1. Mastodon — sigmoid.social TIER_1 English(EN) · [email protected] ·

    LLMs force engineering teams to rethink one of the core assumptions of software testing: deterministic outcomes. At today’s @[email protected] meetup in Bern, se

    LLMs force engineering teams to rethink one of the core assumptions of software testing: deterministic outcomes. At today’s @[email protected] meetup in Bern, senior software engineer Mike Mannion talks about statistical testing for LLMs in Java using PUnit. Sneak Peek: → probabil…

    LINKS jug.ch/…/testing-with-punit_be.html

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