Efficient storage and fast querying of source code

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.

Global IT and IT-enabled services

“…The second set of three papers in this issue is devoted to this area. Panchenko et al (2010) pointed out that it is important to quickly gain deep and detailed insights over a large amount of either internally developed or foreign source code. The three major requirements for a source code repository are the ability to handle a large amount of data, fine granularity, and high speed.…”