AI coding assistants are getting worse? (spectrum.ieee.org)

Article summary

The author, a data scientist, has noticed a decline in the performance of AI coding assistants, with newer models being more prone to silent but deadly failure modes, where they generate code that seems to run successfully but produces incorrect results. The author attributes this decline to the way AI models are trained, with a focus on user acceptance rather than actual correctness. A simple test case was conducted to compare the performance of different AI models, with older models performing better than newer ones. The author suggests that AI coding companies need to invest in high-quality training data to improve the performance of their models.

Main themes

• AI coding assistants
• Model performance decline
• Training data quality
• Silent failure modes
• Code correctness

What commenters say

• The decline in AI coding assistant performance is a real issue that needs to be addressed through better training data and testing methods.
• The problem is not with the AI models themselves, but with the way they are being used and the expectations placed upon them.
• The test case presented in the article is flawed and does not accurately reflect the capabilities of newer AI models.
• The focus on user acceptance rather than correctness is a major contributor to the decline in AI coding assistant performance.
• The use of AI coding assistants without human oversight is a recipe for disaster and can lead to serious errors and security vulnerabilities.
• The article's findings are not surprising, given the known limitations and biases of AI models, and more research is needed to improve their performance and reliability.