AI-Augmented Closed-Loop Quality Engineering: A Reference Architecture for Continuous Software Quality Intelligence
arXiv:2606.08793v1 Announce Type: cross Abstract: The quality of software engineering is still under a challenge due to disjointed processes between requirements, testing, and production, which hinders the opportunity to implement quality strategies in consecutive releases. Existing approaches tend to be fixed-model or single-optimization approaches and lack production feedback learning mechanisms. The paper at hand proposes a closed-loop reference architecture of continuous software quality intelligence with AI enhancements. The model synthesizes requirement feature mining, risk-based test pr
The increasing complexity of software systems and the rapid deployment cycles necessitate more sophisticated and autonomous quality assurance mechanisms, making AI a timely integration.
This development indicates a move towards fully autonomous software development and maintenance, promising higher quality, faster iteration, and reduced human intervention in critical engineering processes.
Software quality engineering will shift from disjointed, human-intensive processes to integrated, AI-augmented closed-loop systems that learn and adapt based on production feedback.
- · Software Development Teams
- · AI/ML Platform Providers
- · Large Enterprises with Complex Software
- · Traditional QA Companies
- · Manual Software Testers
Reduced software defects and faster time-to-market for new features as AI automates quality assurance.
Increased demand for AI engineers and data scientists specialized in software quality and reliability.
The development of entirely autonomous software factories where AI manages the full lifecycle from requirements to deployment and maintenance.
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Read at arXiv cs.AI