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AI Coding AssistantsAI Business & IndustryAI Watch (Impress)Published: Oct 23, 2026, 19:01 JST

DeNA launches DAAQ, cuts test-creation hours up to 90%

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DeNA launches DAAQ, cuts test-creation hours up to 90%

3 Key Points

  1. What happened

    DeNA launched DAAQ, which packages its QA division's testing know-how with in-house AI test tools; internally it says test-creation work hours fell by up to 90%.

  2. Why it matters

    The service aims to speed development while keeping quality, and DeNA's QA staff handle AI input and output follow-up so users can just give instructions and check results, with human review at each step.

WHO IT HITSSoftware development and QA teams that want faster testing without mastering new tools may find this relevant, as DeNA's staff handle AI follow-up and users only give instructions and check results.

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Context & Analysis

DAAQ packages testing know-how that DeNA's quality control division built across games, live streaming, sports, and other services, together with AI test tools developed in-house. The internal result cited is a reduction of up to 90% in test-creation work hours. Beyond providing tools, DeNA's QA specialists support AI inputs and outputs, and outputs at each step are reviewed by people to ensure quality is not left entirely to AI. User data is handled under three guardrails—tool safety, input protection, and output assurance—and is not used for AI training. Alongside the launch, DeNA signed a partner contract with Digital Hearts, which provides software verification and quality assurance; Digital Hearts will offer verification services using DAAQ.

FAQ
What exactly does DAAQ do?
It covers test creation, test automation, content ethics review, and app store review, using AI to handle repetitive work.
Do users need to learn how to operate the AI tools?
No, DeNA's QA staff follow up on AI inputs and outputs, so users can just give instructions and check results.
How is user data handled?
DAAQ operates under three guardrails: tool safety, input protection, and output assurance; user information is not used for AI training.
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