Neural Transition-based Parsing of Library Deprecations

• URL: http://arxiv.org/abs/2212.12584v1
• Date: Fri, 23 Dec 2022 20:48:33 GMT
• Title: Neural Transition-based Parsing of Library Deprecations
• Authors: Petr Babkin, Nacho Navarro, Salwa Alamir, Sameena Shah
• Abstract summary: This paper tackles the 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 text processes those documents into tree-structured representations. 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.
• Score: 3.6382354548339295
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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