  This paper tackles the challenging problem of automating code updates to fix
deprecated API usages of open source libraries by analyzing their release
notes. Our system employs a three-tier architecture: first, a web crawler
service retrieves deprecation documentation from the web; then a specially
built parser processes those text documents into tree-structured
representations; finally, a client IDE plugin locates and fixes identified
deprecated usages of libraries in a given codebase. The focus of this paper in
particular is the parsing component. We introduce a novel transition-based
parser in two variants: based on a classical feature engineered classifier and
a neural tree encoder. To confirm the effectiveness of our method, we gathered
and labeled a set of 426 API deprecations from 7 well-known Python data science
libraries, and demonstrated our approach decisively outperforms a non-trivial
neural machine translation baseline.
