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Efficient storage and fast querying of source code

Author

Listed:
  • Oleksandr Panchenko

    (Hasso Plattner Institute for Software Systems Engineering)

  • Hasso Plattner

    (Hasso Plattner Institute for Software Systems Engineering)

  • Alexander B. Zeier

    (Hasso Plattner Institute for Software Systems Engineering)

Abstract

Enabling fast and detailed insights over large portions of source code is an important task in a global development ecosystem. Numerous data structures have been developed to store source code and to support various structural queries, to help in navigation, evaluation and analysis. Many of these data structures work with tree-based or graph-based representations of source code. The goal of this project is to elaborate a data storage that enables efficient storing and fast querying of structural information. The naive adjacency list method has been enhanced with the use of recent data compression approaches for column-oriented databases to allow no-loss albeit compact storage of fine-grained structural data. The graph indexing has enabled the proposed data model to expeditiously answer fine-grained structural queries. This paper describes the basics of the proposed approach and illustrates its technical feasibility.

Suggested Citation

  • Oleksandr Panchenko & Hasso Plattner & Alexander B. Zeier, 2011. "Efficient storage and fast querying of source code," Information Systems Frontiers, Springer, vol. 13(3), pages 349-357, July.
    • Handle: RePEc:spr:infosf:v:13:y:2011:i:3:d:10.1007_s10796-010-9285-6
    DOI: 10.1007/s10796-010-9285-6
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    Cited by:

    1. Chulhwan Chris Bang, 2015. "Information systems frontiers: Keyword analysis and classification," Information Systems Frontiers, Springer, vol. 17(1), pages 217-237, February.

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