SOAR: A Synthesis Approach for Data Science API Refactoring
Reviewed by Ravika Nagpal / 2021-11-18
Keywords: Automation, Data Science, Maintenance
Everything that has been constructed eventually needs maintenance: homes, parks, offices---and code. Refactoring is one way programmers do this. Though the idea is simple, it can quickly become a programmer's nightmare, as manual refactoring is tedious and error-prone.
Ni2021 introduce an automated refactoring technique called SOAR that combines natural language processing with program synthesis to automatically migrate and refactor between different versions of APIs. It begins by constructing an API matching model based on available documentation for the source and target libraries to find potential replacement calls for each API call in the source program. It then employs program synthesis to build the whole target method call that closely resembles the original behavior. Retaining the behavior ensures that the program's functionality is preserved.
The authors chose two well-documented libraries---TensorFlow to Pytorch---for evaluations. Similarly, as an example of library migration between different languages, the authors looked at migrating from dplyr in R to pandas in Python. These examples are easy to follow and help readers understand how SOAR stands out from other work in this area:
- Prior work in automatic API migration mostly focused on example-based migration techniques, that is, on learning API migration patterns using code examples. SOAR can migrate without existing code examples.
- Earlier works relied on the training data for cross-language API mappings, whereas SOAR leverages migration documentation.
- SOAR uses interpreter error messages to restrict the domain of the parameters and prune the search space, not to refine type information.
SOAR shows that library refactoring automation in data science is both feasible and helpful. We look forward to seeing where the field goes next.
Ni2021 Ansong Ni, Daniel Ramos, Aidan Z. H. Yang, Ines Lynce, Vasco Manquinho, Ruben Martins, and Claire Le Goues: "SOAR: A Synthesis Approach for Data Science API Refactoring". Proc. International Conference on Software Engineering (ICSE), 2021, 10.1109/icse43902.2021.00023.
With the growth of the open-source data science community, both the number of data science libraries and the number of versions for the same library are increasing rapidly. To match the evolving APIs from those libraries, open-source organizations often have to exert manual effort to refactor the APIs used in the code base. Moreover, due to the abundance of similar open-source libraries, data scientists working on a certain application may have an abundance of libraries to choose, maintain and migrate between. The manual refactoring between APIs is a tedious and error-prone task. Although recent research efforts were made on performing automatic API refactoring between different languages, previous work relies on statistical learning with collected pairwise training data for the API matching and migration. Using large statistical data for refactoring is not ideal because such training data will not be available for a new library or a new version of the same library. We introduce Synthesis for OpenSource API Refactoring (SOAR), a novel technique that requires no training data to achieve API migration and refactoring. SOAR relies only on the documentation that is readily available at the release of the library to learn API representations and mapping between libraries. Using program synthesis, SOAR automatically computes the correct configuration of arguments to the APIs and any glue code required to invoke those APIs. SOAR also uses the interpreter's error messages when running refactored code to generate logical constraints that can be used to prune the search space. Our empirical evaluation shows that SOAR can successfully refactor 80% of our benchmarks corresponding to deep learning models with up to 44 layers with an average run time of 97.23 seconds, and 90% of the data wrangling benchmarks with an average run time of 17.31 seconds.