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In this session, we’ll take an engaging journey through real-world lessons learned while bridging the gap between AI-powered testing prototypes and robust, production-ready solutions. I'll discuss the practical challenges that arise when moving from proof of concept (PoC) to full productification, including the hurdles faced and some failed PoCs that provided invaluable insights. We’ll dive into topics such as strategic test case selection, tackling the notorious challenge of flaky tests,
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This spring, we are excited with a series of online events where companies present their experiences of AI. On April the 3rd, we have Azeem Ahmad from Ericsson Linköping, who will present their experiences with AI. |
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We are pleased to invite you to an open workshop presenting our ongoing research project, conducted in collaboration with WASP, on quality assurance for machine-learning (ML) programs and notebooks. Machine-learning software differs fundamentally from traditional software in development practices, organizational roles, and strong dependency on training data. However, software quality assurance (QA) aiming to ensure robustness, maintainability, and functional correctness is much less established than those for engineering traditional software. |
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Compliance with Standards — An Agentic Approach This presentation argues that ensuring product compliance with large standards organizations becomes unmanageable when handled manually, due to the sheer scale of standards and their heterogeneous formats, while simultaneously demanding rigorous traceability, coverage, and compliance. To address this challenge, we introduce an AI-assisted, agentic workflow that treats standards as a searchable corpus and applies a structured pipeline to support requirements engineering at scale. |
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