Generative AI in Software Engineering: Adoption, Hype, and Reality Check

Generative AI is the latest tech darling software engineering research. A recent survey of 457 researchers, publishing in top venues between 2023 and 2025, reveals that the AI is both loved and loathed. Researchers are feeling the heat to jump on board, but the journey isn’t as smooth as it seems.

Pressure to Adopt

Many researchers report pressure to incorporate GenAI into their work. It's like a badge of honor or a necessary evil, depending on who you ask. Some are using it to spruce up writing and kickstart projects. But the heavy lifting, methodological and analytical tasks, human brains still lead the charge.

The hype around productivity gains is real. Yet, lurking in the shadows are concerns about trust and correctness. How much of what GenAI churns out can we really trust? Amidst regulatory uncertainty, researchers are left with more questions than answers.

Beneath the Surface

Digging deeper, the excitement around GenAI is tempered by serious doubts. Risks like inaccuracies and biases are top of mind. The solution? Human oversight and verification, researchers say. But isn't that doubling the work instead of easing it?

Calls for clearer governance echo through the halls of academia. The need for guidance on responsible use and peer review is loud and clear. Without it, integrating GenAI responsibly remains a pipe dream.

Reality Check

So, what's the verdict? Generative AI in software engineering is more than just a passing fad, but it's not the silver bullet either. It's a tool, not a savior. Its integration into research practices is a balancing act, pushing the boundaries of what's possible while keeping one foot firmly planted in reality.

Will GenAI redefine software engineering research or simply add another layer of complexity? Show me the retention numbers. Until then, skepticism lingers.