학술논문

MELT: Mining Effective Lightweight Transformations from Pull Requests
Document Type
Conference
Source
2023 38th IEEE/ACM International Conference on Automated Software Engineering (ASE) ASE Automated Software Engineering (ASE), 2023 38th IEEE/ACM International Conference on. :1516-1528 Sep, 2023
Subject
Computing and Processing
Codes
Manuals
Libraries
Software
Data mining
Software engineering
software refactoring
api migration
Language
ISSN
2643-1572
Abstract
Software developers often struggle to update APIs, leading to manual, time-consuming, and error-prone processes. We introduce Melt, a new approach that generates lightweight API migration rules directly from pull requests in popular library repositories. Our key insight is that pull requests merged into open-source libraries are a rich source of information sufficient to mine API migration rules. By leveraging code examples mined from the library source and automatically generated code examples based on the pull requests, we infer transformation rules in Comby, a language for structural code search and replace. Since inferred rules from single code examples may be too specific, we propose a generalization procedure to make the rules more applicable to client projects. Melt rules are syntax-driven, interpretable, and easily adaptable. Moreover, unlike previous work, our approach enables rule inference to seamlessly integrate into the library workflow, removing the need to wait for client code migrations. We evaluated Melt on pull requests from four popular libraries, successfully mining 461 migration rules from code examples in pull requests and 114 rules from auto-generated code examples. Our generalization procedure increases the number of matches for mined rules by 9×. We applied these rules to client projects and ran their tests, which led to an overall decrease in the number of warnings and fixing some test cases demonstrating MELT's effectiveness in real-world scenarios.