Imagine deciphering complex code in a flash. That's the magic of automatic code summarization, a game-changer explored by Ericsson researchers in their latest work. They investigated how AI, specifically large language models (LLMs), can generate concise summaries of Java methods—those crucial building blocks of software. These AI-powered summaries act like instant cheat sheets, explaining what a block of code does without needing to dissect every line. The team tested several AI prompting techniques, from simple requests to more nuanced instructions, to see which generated the most accurate and useful summaries. Surprisingly, the simpler methods often outperformed the more complex ones, generating summaries that were both concise and insightful. They tested their approaches not just on Ericsson's code but also on widely used open-source projects like Guava and Elasticsearch. One intriguing finding was the importance of method names. When the AI didn't know the method's name, the quality of the summary often suffered. This suggests that AI draws on a range of cues to understand code, just like human developers. This research has the potential to dramatically speed up software development, improve code comprehension, and ultimately make software maintenance more efficient. It’s like getting the icing on the code cake—a sweet layer of understanding on top of complex software projects.