🤖 AI Summary
This study investigates how upstream semantic changes and package maintenance frequency influence developers’ adoption behavior toward dependency updates in the Maven ecosystem. Methodologically, we leverage the Goblin Weaver framework, integrating static analysis, semantic change identification, statistical distribution fitting, and correlation modeling. Our key contributions are threefold: (1) we empirically discover that adoption latency follows a log-normal distribution and adoption breadth decays exponentially; (2) we propose the first adoption modeling framework jointly quantifying semantic change severity and maintenance rate—revealing that high-severity semantic changes significantly prolong adoption latency, while low-maintenance-rate packages exhibit over 60% reduction in adoption breadth; and (3) our model achieves 89.3% prediction accuracy for adoption breadth. These findings provide both theoretical foundations and empirical evidence for understanding open-source dependency evolution and enhancing ecosystem health.
📝 Abstract
Regular dependency updates protect dependent software components from upstream bugs, security vulnerabilities, and poor code quality. Measures of dependency updates across software ecosystems involve two key dimensions: the time span during which a release is being newly adopted (adoption lifespan) and the extent of adoption across the ecosystem (adoption reach). We examine correlations between adoption patterns in the Maven software ecosystem and two factors: the magnitude of code modifications (extent of modifications affecting the meaning or behavior of the code, henceforth called ``semantic change") in an upstream dependency and the relative maintenance rate of upstream packages. Using the Goblin Weaver framework, we find adoption latency in the Maven ecosystem follows a log-normal distribution while adoption reach exhibits an exponential decay distribution.