Adaptoring: Adapter Generation to Provide an Alternative API for a Library

Abstract

Third-party libraries are a cornerstone of fast application development. To enable efficient use, libraries must provide a well-designed API. An obscure API instead slows down the learning process and can lead to erroneous use. The usual approach to improve the API of a library is to edit its code directly, either keeping the old API but deprecating it (temporarily increasing the API size) or dropping it (introducing breaking changes). If maintainers are unwilling to make such changes, others need to create a hard fork, which they can refactor. But then it is difficult to incorporate changes to the original library, such as bug fixes or performance improvements. In this paper, we instead explore the use of the adapter pattern to provide a new API as a new library that calls the original library internally. This allows the new library to leverage all implementation changes to the original library, at no additional cost. We call this approach adaptoring. To make the approach practical, we identify API transformations for which adapter code can be generated automatically, and investigate which transformations can be inferred automatically, based on the documentation and usage patterns of the original library. For cases where automated inference is not possible, we present a tool that lets developers manually specify API transformations. Finally, we consider the issue of migrating the generated adapters if the original library introduces breaking changes. We implemented our approach for Python, demonstrating its effectiveness to quickly provide an alternative API even for large libraries.