Metrics

Metrics blog

• Can we force LLMs to generate the code we really want? June 18, 2026
  Experiment design – from the paper Large Language Models (LLMs) are revolutionary for programming productivity, producing functional code snippets in seconds. However, as software engineers, my co-authors and I know that “functional” is not the same as “well-designed.” LLMs are generally “bottom-up” thinkers; they excel at local syntax but struggle to adhere to higher-level architectural […]
  Miroslaw Staron
• My prompt is better than your prompt – how to optimize your prompts in the age of agentic AI June 12, 2026
  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. This is even more important now that we have agents which work for days on our tasks. […]
  Miroslaw Staron
• 15 years of Software Center – A Look in the Mirror and over the Front Windshield June 10, 2026
  Image source: Gemini, based on the summary of this blog post. When I write this post, I’m sitting at a reporting workshop of Software Center, at Axis Communications in Lund. Jan has reminded us that we’ve been going on for 15 years. That’s most of my academic career and a lot of my life. Although […]
  Miroslaw Staron
• Junior Architects with Shaky Logic: Testing AI’s Real-World Coding Skills – article review June 5, 2026
  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. AI coding editors like GitHub Copilot are increasingly adopted, with studies suggesting that up to 88% of developers report increased productivity. But accelerations in development come […]
  Miroslaw Staron
• The Synthetic Engineer: Measuring the Real Impact of AI on Software Delivery May 18, 2026
  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. Software engineers adopt AI increasingly often and increasingly deep. However, as organizations pour investment into Generative AI tools, a critical question remains: How do we measure the true return on investment? I asked Gemini to analyze […]
  Miroslaw Staron