Data science libraries are updated frequently, and new version releases commonly include breaking changes. However, developers often use older versions of libraries because it is challenging to update the source code of large projects. We propose CombyInferPy, a new tool for analyzing and fixing breaking changes in library APIs. CombyInferPy infers rules in the form of Comby templates, a structural code search and replace tool that can automatically update source code. Preliminary results show that CombyInferPy can update the pandas library’s Python code. Using the Comby rules inferred by CombyInferPy, we can automatically fix several failing tests and deprecation warnings. This shows that this approach is promising and can help developers update their libraries.

Hailie Mitchell

Carnegie Mellon University