Software frameworks and libraries are indispensable to today’s software systems. As they evolve, it is often timeconsuming for developers to keep their code up-to-date, so approaches have been proposed to facilitate this. Usually, these approaches cannot automatically identify change rules for one-replaced-by-many and many-replaced-by-one methods, and they trade off recall for higher precision using one or more experimentally-evaluated thresholds. We introduce AURA, a novel hybrid approach that combines call dependency and text similarity analyses to overcome these limitations. We implement it in a Java system and compare it on five frameworks with three previous approaches by Dagenais and Robillard, M. Kim et al., and Sch¨afer et al. The comparison shows that, on average, the recall of AURA is 53.07% higher while its precision is similar, e.g., 0.10% lower.