research-article

Toward deep learning software repositories

Authors:

Christopher Vendome,

Mario Linares-Vásquez,

Denys PoshyvanykAuthors Info & Claims

MSR '15: Proceedings of the 12th Working Conference on Mining Software Repositories

Pages 334 - 345

Published: 16 May 2015 Publication History

Abstract

Deep learning subsumes algorithms that automatically learn compositional representations. The ability of these models to generalize well has ushered in tremendous advances in many fields such as natural language processing (NLP). Recent research in the software engineering (SE) community has demonstrated the usefulness of applying NLP techniques to software corpora. Hence, we motivate deep learning for software language modeling, highlighting fundamental differences between state-of-the-practice software language models and connectionist models. Our deep learning models are applicable to source code files (since they only require lexically analyzed source code written in any programming language) and other types of artifacts. We show how a particular deep learning model can remember its state to effectively model sequential data, e.g., streaming software tokens, and the state is shown to be much more expressive than discrete tokens in a prefix. Then we instantiate deep learning models and show that deep learning induces high-quality models compared to n-grams and cache-based n-grams on a corpus of Java projects. We experiment with two of the models' hyperparameters, which govern their capacity and the amount of context they use to inform predictions, before building several committees of software language models to aid generalization. Then we apply the deep learning models to code suggestion and demonstrate their effectiveness at a real SE task compared to state-of-the-practice models. Finally, we propose avenues for future work, where deep learning can be brought to bear to support model-based testing, improve software lexicons, and conceptualize software artifacts. Thus, our work serves as the first step toward deep learning software repositories.

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cover image ACM Conferences

MSR '15: Proceedings of the 12th Working Conference on Mining Software Repositories

May 2015

542 pages

ISBN:9780769555942

General Chair:
Massimiliano Di Penta
University of Sannio, Italy
,
Program Chairs:
Martin Pinzger
University of Klagenfurt, Austria
,
Romain Robbes
University of Chile, Chile

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ACM: Association for Computing Machinery
SIGSOFT: ACM Special Interest Group on Software Engineering
IEEE-CS\DATC: IEEE Computer Society
TCSE: IEEE Computer Society's Tech. Council on Software Engin.

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IEEE Press

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Published: 16 May 2015

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May 16 - 24, 2015

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