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Learning effective representations of source code is critical for any MachineLearning for Software Engineering (ML4SE) system. Inspired by natural languageprocessing, large language models (LLMs) like Codex and CodeGen treat code asgeneric sequences of text and are trained on huge corpora of code data,achieving state of the art performance on several software engineering (SE)tasks. However, valid source code, unlike natural language, follows a strictstructure and pattern governed by the underlying grammar of the programminglanguage. Current LLMs do not exploit this property of the source code as theytreat code like a sequence of tokens and overlook key structural and semanticproperties of code that can be extracted from code-views like the Control FlowGraph (CFG), Data Flow Graph (DFG), Abstract Syntax Tree (AST), etc.Unfortunately, the process of generating and integrating code-views for everyprogramming language is cumbersome and time consuming. To overcome thisbarrier, we propose our tool COMEX - a framework that allows researchers anddevelopers to create and combine multiple code-views which can be used bymachine learning (ML) models for various SE tasks. Some salient features of ourtool are: (i) it works directly on source code (which need not be compilable),(ii) it currently supports Java and C#, (iii) it can analyze both method-levelsnippets and program-level snippets by using both intra-procedural andinter-procedural analysis, and (iv) it is easily extendable to other languagesas it is built on tree-sitter - a widely used incremental parser that supportsover 40 languages. We believe this easy-to-use code-view generation andcustomization tool will give impetus to research in source code representationlearning methods and ML4SE.  Tool: https://pypi.org/project/comex - GitHub:https://github.com/IBM/tree-sitter-codeviews - Demo:https://youtu.be/GER6U87FVbU

COMEX: A Tool for Generating Customized Source Code Representations