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Efficiently representing source code is crucial for various softwareengineering tasks such as code classification and clone detection. Existingapproaches primarily use Abstract Syntax Tree (AST), and only a few focus onsemantic graphs such as Control Flow Graph (CFG) and Program Dependency Graph(PDG), which contain information about source code that AST does not. Eventhough some works tried to utilize multiple representations, they do notprovide any insights about the costs and benefits of using multiplerepresentations. The primary goal of this paper is to discuss the implicationsof utilizing multiple code representations, specifically AST, CFG, and PDG. Wemodify an AST path-based approach to accept multiple representations as inputto an attention-based model. We do this to measure the impact of additionalrepresentations (such as CFG and PDG) over AST. We evaluate our approach onthree tasks: Method Naming, Program Classification, and Clone Detection. Ourapproach increases the performance on these tasks by 11% (F1), 15.7%(Accuracy), and 9.3% (F1), respectively, over the baseline. In addition to theeffect on performance, we discuss timing overheads incurred with multiplerepresentations. We envision this work providing researchers with a lens toevaluate combinations of code representations for various tasks.

On the Impact of Multiple Source Code Representations on Software Engineering Tasks -- An Empirical Study