Ahmed Khaled
Google Summer of Code 2025 Contributor @ openSUSE
Cairo, Egypt
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Ahmed is an Egyptian final-year Computer Science student at Ain Shams University. His software engineering journey started at 16 when a school friend introduced him to developing Discord bots. He went on to create his own bot with subscription plans and made his first $1000, an early milestone that got him hooked on building things that people actually use.
When he entered university, he joined an open source-focused student activity where a senior’s obsession with Git inspired him to fall in love with Git and open source. That spark grew into something bigger when he was accepted into Google Summer of Code 2024 with Synfig, working on projects involving Git, DevOps, Scripting, and Linux. There, he discovered his passion for automation. In the summer of 2025, he returned to Google Summer of Code, this time with openSUSE, on the project he will be presenting at this conference.
Ahmed loves low-level courses, with operating systems being his favorite, and he has a habit of digging into how things work under the hood. He loves studying, staying up to date with industry advancements, exploring startups, and watching software engineering or scientific videos while eating. Outside of tech, he enjoys music, playing tennis, walking, and hiking.
Area of Expertise
Topics
First Open Source Implementation Of Facebook's AI-Driven PR Test Selection
AI-Driven Pull Request Test Selection, technically known as Predictive Test Selection (PTS), is a machine learning approach that, using past pull requests, test results, and other relevant information, predicts which small subset of tests is most likely to fail for a new pull request. Running only this smaller subset of tests speeds up developer feedback, reduces infrastructure costs, and maintains high test coverage. Inspired by Facebook’s research paper on PTS, we implemented this technique from the ground up inside Uyuni, a large open-source project under openSUSE.
Unlike the few commercial offerings, the work presented in this talk is 100% open source. All the code, techniques, design decisions, and engineering challenges are publicly available for you to learn from and reuse. We built this system from scratch, initially not even storing PR test results. We will show you how we went step by step, collecting and maintaining PR test data, engineering machine learning features, training and deploying the model, and monitoring its performance in production. Along the way, you will see the challenges we faced, how we solved them, and the results we obtained.
By attending this session, you will:
- Understand what predictive test selection is, its benefits, and how it works.
- See a live demo of predictive test selection in action.
- See a complete, practical reference of how we implemented it and learn how to adapt or reuse parts of it in your own CI and test frameworks.
- Learn from the challenges we faced, most of which apply broadly to software engineering, continuous integration, machine learning, and testing, not just predictive test selection.
- Know whether predictive test selection is worth implementing and whether it is right for your organization.
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