PSIMiner: A Tool for Mining Rich Abstract Syntax Trees from Code
This work addresses the need for better input data in ML-based code analysis, though it is incremental as it builds on existing IDE capabilities and models.
The authors tackled the problem of enriching code representations for machine learning by developing PSIMiner, a tool that processes PSI trees from the IntelliJ Platform to mine rich abstract syntax trees, and demonstrated its utility by extending the code2seq model for method name prediction in Java.
The application of machine learning algorithms to source code has grown in the past years. Since these algorithms are quite sensitive to input data, it is not surprising that researchers experiment with input representations. Nowadays, a popular starting point to represent code is abstract syntax trees (ASTs). Abstract syntax trees have been used for a long time in various software engineering domains, and in particular in IDEs. The API of modern IDEs allows to manipulate and traverse ASTs, resolve references between code elements, etc. Such algorithms can enrich ASTs with new data and therefore may be useful in ML-based code analysis. In this work, we present PSIMiner - a tool for processing PSI trees from the IntelliJ Platform. PSI trees contain code syntax trees as well as functions to work with them, and therefore can be used to enrich code representation using static analysis algorithms of modern IDEs. To showcase this idea, we use our tool to infer types of identifiers in Java ASTs and extend the code2seq model for the method name prediction problem.