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International Andrei Ershov Memorial Conference on Perspectives of System Informatics

PSI 2017: Perspectives of System Informatics pp 352–363Cite as

Statistical Approach to Increase Source Code Completion Accuracy

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10742))

Abstract

Code completion is an essential feature in every IDE’s toolbox, boosting a developer’s productivity and significantly reducing time spent on code exploration. In this paper, we introduce the extension of a typical code completion system. At each point, we construct a list of all possible functions, which are then sorted according to our probabilistic model. We draw our inspiration from natural language processing (NLP). As the foundation, we select the N-gram model, which works on top of abstract syntax tree (AST) nodes. Since our approach is not bound to any other analyses, our model is language-agnostic, and thus, can be applied to any programming language. Experiments on several well-known open source projects show that the described method is sound. It has an execution time comparable to naïve approaches and achieves much more accurate results.

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Notes

  1. 1.

    https://clang.llvm.org/docs/LibTooling.html.

  2. 2.

    https://clang.llvm.org/docs/JSONCompilationDatabase.html.

  3. 3.

    http://www.llvm.org/.

  4. 4.

    https://www.mysql.com/.

  5. 5.

    http://www.opencv.org/.

  6. 6.

    http://www.caffe.berkeleyvision.org/.

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Author information

Authors and Affiliations

  1. Institute for System Programming of the Russian Academy of Sciences, 25, Alexander Solzhenitsyn street, Moscow, 109004, Russian Federation

    Valeriy Savchenko

  2. Lomonosov Moscow State University, GSP-1, Leninskie Gory, Moscow, 119991, Russian Federation

    Alexander Volkov

Authors
  1. Valeriy Savchenko
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  2. Alexander Volkov
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Corresponding authors

Correspondence to Valeriy Savchenko or Alexander Volkov .

Editor information

Editors and Affiliations

  1. Ivannikov Institute for System Programming of RAS, Moscow, Russia

    Alexander K. Petrenko

  2. The University of Manchester, Manchester, United Kingdom

    Andrei Voronkov

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Savchenko, V., Volkov, A. (2018). Statistical Approach to Increase Source Code Completion Accuracy. In: Petrenko, A., Voronkov, A. (eds) Perspectives of System Informatics. PSI 2017. Lecture Notes in Computer Science(), vol 10742. Springer, Cham. https://doi.org/10.1007/978-3-319-74313-4_25

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  • DOI: https://doi.org/10.1007/978-3-319-74313-4_25

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-74312-7

  • Online ISBN: 978-3-319-74313-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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