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In the era of Big Code, when researchers seek to study an increasingly largenumber of repositories to support their findings, the data processing stage mayrequire manipulating millions and more of records.  In this work we focus on studies involving fine-grained AST level source codechanges. We present how we extended the CodeDistillery source code miningframework with data manipulation capabilities, aimed to alleviate theprocessing of large datasets of fine grained source code changes. Thecapabilities we have introduced allow researchers to highly automate theirrepository mining process and streamline the data acquisition and processingphases. These capabilities have been successfully used to conduct a number ofstudies, in the course of which dozens of millions of fine-grained source codechanges have been processed.

Processing Large Datasets of Fined Grained Source Code Changes