<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Software Engineering Research on Software Engineering Group</title><link>https://seg.inf.unibe.ch/keywords/software-engineering-research/</link><description>Recent content in Software Engineering Research 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/software-engineering-research/index.xml" rel="self" type="application/rss+xml"/><item><title>IDE plugin and middleware for Studying AI-Assisted Software Development</title><link>https://seg.inf.unibe.ch/teaching/student-projects/current/t0084-ide-middleware-for-prompt-analysis/</link><pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate><guid>https://seg.inf.unibe.ch/teaching/student-projects/current/t0084-ide-middleware-for-prompt-analysis/</guid><description>AI coding assistants are now widely used in professional software development. The prompts developers write, the responses they receive, the subsequent edits they make, and the rework they perform before committing code remain almost entirely invisible for in-depth analysis. Without access to the full interaction between developer intent (i.e., a prompt) and AI output, it is not possible to study whether AI-generated code actually addresses what the developer needed.</description></item></channel></rss>