SE metrics (Software Engineering)

Alternatively: do not trust the big tech on how good the AI is – humans are still needed to point it to the right direction Image by Michael Kauer from Pixabay My summer is getting dangerously close to its finish line, despite my strong protests.

Image by Manueldesign20 from Pixabay It’s hard to believe that 2026 is already halfway and the summer is upon us.

Image generated by Gemini based on this blow post https://www.mdpi.com/2076-3417/16/10/4788 The practical meaning of automated code generation is shifting rapidly.

Image taken directly from the paper https://arxiv.org/pdf/2605.24138 The Software Engineering (SE) landscape is shifting from LLM-assisted workflows, like copilots, toward Autonomous SE, where multiple specialized AI agents cooperate without a human in the

Experiment design – from the paper Large Language Models (LLMs) are revolutionary for programming productivity, producing functional code snippets in seconds.

Image generated by Gemini based on the content of this post https://arxiv.org/pdf/2605.19102 Getting Large Language Models (LLMs) to write functional code often feels like casting spells; a slight misphrasing in your prompt can result in a buggy output.

Image source: Gemini, based on the summary of this blog post.

Image generated by Gemini based on the blog post content https://arxiv.org/pdf/2604.23340 We have all seen Large Language Models (LLMs) write impressive snippets of code or debug a tricky function.

https://miroslawstaron.github.io/hallucinations.html#/5 The shift from manual coding to AI-augmented orchestration is no longer a future – it is a reality.

Link to the book I’m often asked what invention I think is the biggest in human history.