Quantifying the benefits of code hints for refactoring deprecated Java APIs

作     者：David, Cristina Kesseli, Pascal Kroening, Daniel Zhang, Hanliang 

作者机构：University of Bristol Bristol United Kingdom Meta Zurich Switzerland Amazon Inc. Seattle United States 

出 版 物：《arXiv》 (arXiv)

年 卷 期：2024年

核心收录：

主　　题：Java programming language 

摘      要：When done manually by engineers at Amazon and other companies, refactoring legacy code in order to eliminate uses of deprecated APIs is an error-prone and time-consuming process. In this paper, we investigate to which degree refactorings for deprecated Java APIs can be automated, and quantify the benefit of Javadoc code hints for this task. To this end, we build a symbolic and a neural engine for the automatic refactoring of deprecated APIs. The former is based on type-directed and component-based program synthesis, whereas the latter uses LLMs. We applied our engines to refactor the deprecated methods in the Oracle JDK 15. Our experiments show that code hints are enabling for the automation of this task: even the worst engine correctly refactors 71% of the tasks with code hints, which drops to at best 14% on tasks without. Adding more code hints to Javadoc can hence boost the refactoring of code that uses deprecated APIs. © 2024, CC BY.