Jan 7, 2026 01:09 AM
(This post was last modified: Jan 7, 2026 01:16 AM by C C.)
https://www.eurekalert.org/news-releases/1111731
INTRO: At first glance, artificial intelligence looks like a software developer’s dream. A recent McKinsey & Company report found that programmers generated code up to 45% faster with the help of generative AI.
But if it’s not used strategically, AI can become a developer’s nightmare. So says Edward Anderson Jr., professor of information, risk, and operations management and Betty and Glenn Mortimer Centennial Professor in Business at Texas McCombs.
The problem arises when AI is used to write code that interacts with so-called legacy systems with outdated software, he explains. These environments are often awash in shortcuts, quick fixes, and other poor programming practices.
Such technical debt costs U.S. companies $1.5 trillion in reduced productivity and cybercrime, estimates the Consortium for Information & Software Quality. It can also lead to real-world meltdowns. In 2022, Southwest Airlines’ 20-year-old scheduling system crashed, stranding passengers on nearly 17,000 flights.
But carelessly using AI to patch such systems risks making them even worse, Anderson warns. For one thing, AI trains on existing code, with all its defects. Thus, it tends to create more technical debt per line of code than trained, experienced human software engineers would.
How can companies avoid such problems? Anderson — with Geoffrey Parker of Dartmouth College and Burcu Tan of the University of New Mexico — interviewed dozens of programmers in a variety of industries. He offers some best practices for AI-assisted software development... (MORE - details, no ads)
PAPER: http://dx.doi.org/10.63383/hadW7619
INTRO: At first glance, artificial intelligence looks like a software developer’s dream. A recent McKinsey & Company report found that programmers generated code up to 45% faster with the help of generative AI.
But if it’s not used strategically, AI can become a developer’s nightmare. So says Edward Anderson Jr., professor of information, risk, and operations management and Betty and Glenn Mortimer Centennial Professor in Business at Texas McCombs.
The problem arises when AI is used to write code that interacts with so-called legacy systems with outdated software, he explains. These environments are often awash in shortcuts, quick fixes, and other poor programming practices.
Such technical debt costs U.S. companies $1.5 trillion in reduced productivity and cybercrime, estimates the Consortium for Information & Software Quality. It can also lead to real-world meltdowns. In 2022, Southwest Airlines’ 20-year-old scheduling system crashed, stranding passengers on nearly 17,000 flights.
But carelessly using AI to patch such systems risks making them even worse, Anderson warns. For one thing, AI trains on existing code, with all its defects. Thus, it tends to create more technical debt per line of code than trained, experienced human software engineers would.
How can companies avoid such problems? Anderson — with Geoffrey Parker of Dartmouth College and Burcu Tan of the University of New Mexico — interviewed dozens of programmers in a variety of industries. He offers some best practices for AI-assisted software development... (MORE - details, no ads)
PAPER: http://dx.doi.org/10.63383/hadW7619