Why should you care?
How do we easily and scalably patch 100,000s of lines of source code? Read about how we used a simple yet powerful data structure – Abstract Syntax Tree (AST) to create a system that from one single central point, maps source code dependencies and in-turn patches all dependencies.
A software system is usually built with assumptions around how dependencies such as the underlying language system, frameworks, libraries etc. are written. Changes in these dependencies may have a ripple effect into the software system itself. For example, recently, the famous Python package pandas released its 1.0.0 version, which has deprecated and changed several functionalities that existed in its previous 0.25.x version. An organization may have many systems using 0.25.x version of pandas. Hence, upgrading it to 1.0.0 will require developers of every system to go through the pandas change documentation and patch their code accordingly.
Since we developers love to automate tedious tasks, it is natural for us to think of writing a patch script that will update the source code of all the systems according to the changes in new pandas version. A patch script could be parsing the source code and doing some kind of find+replace. But such a patch script will likely be unreliable and not comprehensive. For example, say the patch script needs to change the name of a function get to create wherever it is called in the code base. A simple find+replace will end up replacing the word “get” even if it was not a function call. Another example would be that find+replace will not be able to handle cases where code statements spill over to multiple lines. We need the patch script to parse the source code, while understanding the language constructs. In this article, we propose the use of Abstract Syntax Trees (AST) to write such patch scripts. And then later, we present how ASTs can be used to assess code quality…
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