Samenvatting

Using AI to Prioritize Static Analysis FindingsIntroducing static code analysis tools into the development process can be difficult, with an overwhelming number of standards, rules, and practices to navigate. This can make the adoption of static analysis challenging, as unoptimized configurations can lead to false alarms, significant pains, and at worst, teams abandoning the idea of introducing static code analysis tools into their process altogether. To reduce the team’s reliance on the perfect static analysis configuration, and the decision-making burden of the user reviewing warnings, teams can now use AI to automatically identify static analysis warnings that represent the most risk and are most critical to fix. In this presentation, we share how the use of artificial intelligence and machine learning can augment the process of prioritizing warnings, making it much easier to introduce static analysis into the software development lifecycle, and continue to use the technology efficiently.