Dev Conference by Neo4j
Session Track: AI
Session Time:
Session description
Making informed development decisions requires a strong understanding of the connections and complexity within and across your application landscape. With generative AI enabling software changes faster than ever, and dozens of applications to manage within a single enterprise, it is often difficult to have a clear view of how everything fits together. CodeLogic equips engineers with the most comprehensive software dependency data available, combining artifact and runtime scanning to create a complete graph of an enterprise’s application structures. In this session, you will learn how CodeLogic utilizes Neo4j and CypherQL to detect breaking code changes introduced by generative AI and how they can be addressed before a change is accepted into mainline development. Attendees will learn a rare use case for graph and see how CodeLogic models data into simplified maps that can be easily analyzed to identify cross-application dependencies, navigate code change impact, and ultimately reveal the bigger picture.
CTO, CodeLogic
With a razor focus on CI/CD and development best practices, Brandon drives innovation through the application of advanced patterns and methodologies. Brandon’s 25-year background in developing cybersecurity, big data and programming language solutions has provided CodeLogic with a unique deep-inspection, and graph-centric, platform. Prior to joining CodeLogic, Brandon led software engineering for a series of major cybersecurity and physical security solutions.
