A GitHub report on the growth and impact of AI on software development, including claims pf a 55% faster coding when using an AI-assisted automation tool. GitClear published a separate report to determine how AI Assistants influence the quality of code being written.
Just as important, as development teams start to include automation tools in the development cycle, how can they maintain control over the quality of the software, including keeping code secure.
In this PodChats for FutureCIO, we are joined by Kelvin Lim, Senior Director of Security Engineering, Synopsys, to talk about navigating the complexities of AI-generated code.
1. Give us the state of in-house software development today.
2. How is software development evolving in response to the rise in AI-generated code?
3. What are the primary security vulnerabilities inherent in AI-generated code?
4. What are the key considerations that teams/organisations should consider when integrating AI-generated code into their software?
5. How will AI-generated code impact DevSecOp cycle? (report Stanford 2022 – development teams code written by GenAI is less secure)
6. Given the rapid pace of tech innovation, what do you anticipate in the future evolution of GenAI and its implications for software development practices?
7. Ask Mel – data poisoning impact AI code generation.
8. What is your advise for use of Gen AI?