Using LLMs to Evaluate Code

Software Engineering Institute (SEI) Webcast Series

Контент предоставлен Carnegie Mellon University Software Engineering Institute and SEI Members of Technical Staff. Весь контент подкастов, включая эпизоды, графику и описания подкастов, загружается и предоставляется непосредственно компанией Carnegie Mellon University Software Engineering Institute and SEI Members of Technical Staff или ее партнером по платформе подкастов. Если вы считаете, что кто-то использует вашу работу, защищенную авторским правом, без вашего разрешения, вы можете выполнить процедуру, описанную здесь https://ru.player.fm/legal.

2M ago 1:02:10

MP4•Главная эпизода

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 эпизодов

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