自动修复数据科学库的破坏性更改

Proceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering Pub Date : 2022-10-10 DOI:10.1145/3551349.3559507

Hailie Mitchell

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引用次数: 0

摘要

数据科学库经常更新，新版本发布通常包含破坏性更改。这些更新会导致现有代码无法编译或运行。开发人员经常使用旧版本的库，因为更新大型项目的源代码具有挑战性。我们提出CombyInferPy，这是一个自动分析和修复库api中的破坏性更改的新工具。CombyInferPy以Comby模板的形式从库源代码的历史中推断出规则，这是一个可以自动转换代码的结构化代码搜索和替换工具。初步结果表明CombyInferPy可以更新pandas库的Python代码。使用CombyInferPy推断的Comby规则，我们可以自动修复一些失败的测试和警告。这表明这种方法很有希望帮助开发人员更新库。

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Automatically Fixing Breaking Changes of Data Science Libraries

Data science libraries are updated frequently, and new version releases commonly include breaking changes. These are updates that cause existing code to not compile or run. 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 to automatically analyze and fix breaking changes in library APIs. CombyInferPy infers rules from the history of library source code in the form of Comby templates, a structural code search and replace tool that can automatically transform code. Preliminary results indicate CombyInferPy can update the pandas library Python code. Using the Comby rules inferred by CombyInferPy, we can automatically fix several failing tests and warnings. This shows this approach is promising to help developers update libraries.

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Proceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering

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