Neural Transition-based Parsing of Library Deprecations

TL;DR

This paper presents a neural transition-based parser designed to analyze deprecation documentation and automate code updates for deprecated API usages in open source libraries, improving over baseline methods.

Contribution

It introduces a novel neural transition-based parsing approach for processing deprecation texts, enhancing automated code update tools.

Findings

The neural parser outperforms baseline machine translation methods.

Successfully processed 426 deprecations from Python libraries.

Improves automation in updating deprecated API usages.

Abstract

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.