This application relates to the field of
software engineering technology and discloses a method for automated migration of customized code between PLM systems. The method includes: constructing a
program profile containing a static
semantic dependency graph and a dynamic execution trajectory graph; mining behavioral patterns in the dynamic execution trajectory graph and introducing intent entropy for quantitative evaluation to dynamically allocate migration strategies of overall behavior reconstruction or conservative step-by-step mapping; performing
code transformation based on the allocated strategy; verifying the transformed code and triggering a feedback mechanism by identifying non-habitual migrations to achieve self-learning optimization of migration rules. Furthermore, this invention supports incremental migration, determining the
impact domain of code changes through graph difference operations and migrating only the affected parts to adapt to version iterations. By deeply analyzing the business intent of the code and combining self-learning and incremental migration mechanisms, this invention significantly improves the
automation level, accuracy, and version iteration efficiency of
code migration.