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Statistical language modeling and translation with transformers have foundmany successful applications in program understanding and generation tasks,setting high benchmarks for tools in modern software development environments.The finite context window of these neural models means, however, that they willbe unable to leverage the entire relevant context of large files and packagesfor any given task. While there are many efforts to extend the context window,we introduce an architecture-independent approach for leveraging the syntactichierarchies of source code for incorporating entire file-level context into afixed-length window. Using concrete syntax trees of each source file we extractsyntactic hierarchies and integrate them into context window by selectivelyremoving from view more specific, less relevant scopes for a given task. Weevaluate this approach on code generation tasks and joint translation ofnatural language and source code in Python programming language, achieving anew state-of-the-art in code completion and summarization for Python in theCodeXGLUE benchmark. We also introduce new CodeXGLUE benchmarks foruser-experience-motivated tasks: code completion with normalized literals,method body completion/code summarization conditioned on file-level context.

Long-Range Modeling of Source Code Files with eWASH: Extended Window Access by Syntax Hierarchy