Using LLMs to Evaluate Code

Software Engineering Institute (SEI) Webcast Series

Treść dostarczona przez Carnegie Mellon University Software Engineering Institute and SEI Members of Technical Staff. Cała zawartość podcastów, w tym odcinki, grafika i opisy podcastów, jest przesyłana i udostępniana bezpośrednio przez Carnegie Mellon University Software Engineering Institute and SEI Members of Technical Staff lub jego partnera na platformie podcastów. Jeśli uważasz, że ktoś wykorzystuje Twoje dzieło chronione prawem autorskim bez Twojej zgody, możesz postępować zgodnie z procedurą opisaną tutaj https://pl.player.fm/legal.

2M ago 1:02:10

MP4•Źródło odcinka

Finding and fixing weaknesses and vulnerabilities in source code has been an ongoing challenge. There is a lot of excitement about the ability of large language models (LLMs, e.g., GenAI) to produce and evaluate programs. One question related to this ability is: Do these systems help in practice? We ran experiments with various LLMs to see if they could correctly identify problems with source code or determine that there were no problems. This webcast will provide background on our methods and a summary of our results.

What Will Attendees Learn?

• how well LLMs can evaluate source code

• evolution of capability as new LLMs are released

• how to address potential gaps in capability

174 odcinków

#Tech #Cybersecurity #Securecoding #Softwarearchitecture #Softwaredevelopment #Softwareengineering #Carnegie Mellon University Software Engineering Institute #SEI Members of Technical Staff #Video #Podcasting Education