Semantic source code models using identifier embeddings

Vasiliki Efstathiou, Diomidis Spinellis

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

4 Citations (Scopus)

Abstract

The emergence of online open source repositories in the recent years has led to an explosion in the volume of openly available source code, coupled with metadata that relate to a variety of software development activities. As an effect, in line with recent advances in machine learning research, software maintenance activities are switching from symbolic formal methods to data-driven methods. In this context, the rich semantics hidden in source code identifiers provide opportunities for building semantic representations of code which can assist tasks of code search and reuse. To this end, we deliver in the form of pretrained vector space models, distributed code representations for six popular programming languages, namely, Java, Python, PHP, C, C++, and C#. The models are produced using fastText, a state-of-the-art library for learning word representations. Each model is trained on data from a single programming language; the code mined for producing all models amounts to over 13.000 repositories. We indicate dissimilarities between natural language and source code, as well as variations in coding conventions in between the different programming languages we processed. We describe how these heterogeneities guided the data preprocessing decisions we took and the selection of the training parameters in the released models. Finally, we propose potential applications of the models and discuss limitations of the models.

Original languageEnglish
Title of host publicationProceedings - 2019 IEEE/ACM 16th International Conference on Mining Software Repositories, MSR 2019
PublisherIEEE
Pages29-33
Number of pages5
ISBN (Electronic)9781728134123
DOIs
Publication statusPublished - May 2019
Externally publishedYes
Event16th IEEE/ACM International Conference on Mining Software Repositories, MSR 2019 - Montreal, Canada
Duration: 26 May 201927 May 2019

Publication series

NameIEEE International Working Conference on Mining Software Repositories
Volume2019-May
ISSN (Print)2160-1852
ISSN (Electronic)2160-1860

Conference

Conference16th IEEE/ACM International Conference on Mining Software Repositories, MSR 2019
CountryCanada
CityMontreal
Period26/05/1927/05/19

Keywords

  • Code Semantics
  • Fasttext
  • Semantic Similarity
  • Vector Space Models

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