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Giorgos Siamantas | Activities

Displaying member activities 301 - 316 of 316 in total
Giorgos Siamantas
Giorgos Siamantas
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Code Reviews for Testers image
Learn the power of code reviews and how they can help you with your testing
Giorgos Siamantas
Giorgos Siamantas
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Automation In Testing: Designing Automated Checks image
Learn the different building blocks to consider when designing an automated check
Giorgos Siamantas
Giorgos Siamantas
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Giorgos Siamantas
Giorgos Siamantas
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Automation In Testing: Rapidly Learning About Systems image
How can you automate a product if you don't know how it works? In this workshop we explore how to capture and share technical knowledge of a system for an automation strategy
Giorgos Siamantas
Giorgos Siamantas
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TestBash Home 2021 image
Join us this year for our worldwide 24 hours online TestBash software testing conference
Giorgos Siamantas
Giorgos Siamantas
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How to Solve Integration Issues with Contract Testing image
In this workshop, we will see what Consumer-Driven Development is, its benefits, and how Contract Testing allows us to follow it
Giorgos Siamantas
Giorgos Siamantas
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What Is Testing in Production and Why Would We Ever Do That?! image
Testing in production tends to be lauded as the new silver bullet or dismissed as a ridiculous meme, in reality, it is just another technique.
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Giorgos Siamantas
Giorgos Siamantas
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Giorgos Siamantas
Giorgos Siamantas
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Giorgos Siamantas profile image
Giorgos Siamantas
Senior Quality Engineer
He/Him

I'm a Senior Quality Engineer with 10+ years across QA and software engineering, focused on risk-driven delivery in complex systems.

I direct effort where failure matters: customer experience, system behaviour, and cost of change. I combine exploratory testing for high-risk behaviour and unknown failure modes with automation and test architecture for reliable checking.

Quality is an emergent property, best understood through deliberate, incremental change. I focus on creating the conditions for safe experimentation in order to learn from real behaviour and iterate based on evidence. This means building testability and quality culture: reducing friction to testing, cutting noise in quality signals, and aligning around shared criteria.

My doctoral research in audio signal processing concentrated on separating overlapping sources from a single recording, which is an underdetermined problem with many valid outputs and no single correct one. AI evaluation often has a similar shape: many plausible outputs, where the challenge is identifying the signals that matter.

I'm building public work in AI systems evaluation, starting with an evaluation of an LLM-based customer review analysis system for faithfulness and prompt injection robustness. It uses calibrated LLM-as-judge scoring for faithfulness and metamorphic checks for robustness, where an adaptive baseline means the check only fails when the injection changes the output.

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