Using LLMs to Evaluate Code

Software Engineering Institute (SEI) Webcast Series

Вміст надано Carnegie Mellon University Software Engineering Institute and SEI Members of Technical Staff. Весь вміст подкастів, включаючи епізоди, графіку та описи подкастів, завантажується та надається безпосередньо компанією Carnegie Mellon University Software Engineering Institute and SEI Members of Technical Staff або його партнером по платформі подкастів. Якщо ви вважаєте, що хтось використовує ваш захищений авторським правом твір без вашого дозволу, ви можете виконати процедуру, описану тут https://uk.player.fm/legal.

23d ago 1:02:10

MP4•Головна епізоду

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

173 епізодів

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