MELT: Mining Effective Lightweight Transformations from Pull Requests

TL;DR

MELT is a novel approach that automatically mines and generalizes API migration rules from pull requests in open-source libraries, enabling efficient and accurate API updates in client projects.

Contribution

MELT introduces a method to extract, generalize, and apply lightweight API migration rules directly from pull request data, streamlining API updates without waiting for client migrations.

Findings

Successfully mined 575 migration rules from pull requests and auto-generated examples.

Generalization increased rule applicability by 9 times.

Applying rules reduced warnings and fixed test cases in client projects.

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 9x. 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.