<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Executable Verification on Software Engineering Group</title><link>https://seg.inf.unibe.ch/keywords/executable-verification/</link><description>Recent content in Executable Verification on Software Engineering Group</description><generator>Hugo</generator><language>en-US</language><copyright>Software Engineering Group (SEG), [Institute of Computer Science](https://www.inf.unibe.ch/), [University of Bern](https://www.unibe.ch/). All rights reserved.</copyright><atom:link href="https://seg.inf.unibe.ch/keywords/executable-verification/index.xml" rel="self" type="application/rss+xml"/><item><title>Generating Reliable Synthetic Training Data from Code Repositories</title><link>https://seg.inf.unibe.ch/teaching/student-projects/current/t0099-synthetic-data-generation-for-code/</link><pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate><guid>https://seg.inf.unibe.ch/teaching/student-projects/current/t0099-synthetic-data-generation-for-code/</guid><description>Language models can generate programming exercises and solutions, but incorrect answers, weak tests, and repetitive tasks can limit their value as training data. This thesis builds on CodifieRL to generate synthetic coding tasks from real repositories and investigate whether execution-based filtering improves their quality and usefulness for training code models.</description></item></channel></rss>