Representing Programs with Dependency and Function Call Graphs for Learning Hierarchical Embeddings

Vitaly Romanov, Vladimir Ivanov, Giancarlo Succi

Abstract

Any source code can be represented as a graph. This kind of representation allows capturing the interaction between the elements of a program, such as functions, variables, etc. Modeling these interactions can enable us to infer the purpose of a code snippet, a function, or even an entire program. Lately, more and more work appear, where source code is represented in the form of a graph. One of the difficulties in evaluating the usefulness of such representation is the lack of a proper dataset and an evaluation metric. Our contribution is in preparing a dataset that represents programs written in Python and Java source codes in the form of dependency and function call graphs. In this dataset, multiple projects are analyzed and united into a single graph. The nodes of the graph represent the functions, variables, classes, methods, interfaces, etc. Nodes for functions carry information about how these functions are constructed internally, and where they are called from. Such graphs enable training hierarchical vector representations for source code. Moreover, some functions come with textual descriptions (docstrings), which allows learning useful tasks such as API search and generation of documentation.

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Paper Citation


in Harvard Style

Romanov V., Ivanov V. and Succi G. (2020). Representing Programs with Dependency and Function Call Graphs for Learning Hierarchical Embeddings.In Proceedings of the 22nd International Conference on Enterprise Information Systems - Volume 2: ICEIS, ISBN 978-989-758-423-7, pages 360-366. DOI: 10.5220/0009511803600366


in Bibtex Style

@conference{iceis20,
author={Vitaly Romanov and Vladimir Ivanov and Giancarlo Succi},
title={Representing Programs with Dependency and Function Call Graphs for Learning Hierarchical Embeddings},
booktitle={Proceedings of the 22nd International Conference on Enterprise Information Systems - Volume 2: ICEIS,},
year={2020},
pages={360-366},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0009511803600366},
isbn={978-989-758-423-7},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 22nd International Conference on Enterprise Information Systems - Volume 2: ICEIS,
TI - Representing Programs with Dependency and Function Call Graphs for Learning Hierarchical Embeddings
SN - 978-989-758-423-7
AU - Romanov V.
AU - Ivanov V.
AU - Succi G.
PY - 2020
SP - 360
EP - 366
DO - 10.5220/0009511803600366