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Conference Papers Year : 2022

HyperAST: Enabling Efficient Analysis of Software Histories at Scale

Abstract

Syntax Trees (ASTs) are widely used beyond compilers in many tools that measure and improve code quality, such as code analysis, bug detection, mining code metrics, refactoring. With the advent of fast software evolution and multistage releases, the temporal analysis of an AST history is becoming useful to understand and maintain code. However, jointly analyzing thousands versions of ASTs independently faces scalability issues, mostly combinatorial, both in terms of memory and CPU usage. In this paper, we propose a novel type of AST, called HyperAST , that enables efficient temporal code analysis on a given software history by: 1/ leveraging code redundancy through space (between code elements) and time (between versions); 2/ reusing intermediate computation results. We show how the HyperAST can be built incrementally on a set of commits to capture all multiple ASTs at once in an optimized way. We evaluated the HyperAST on a curated list of large software projects. Compared to Spoon, a state-of-the-art technique, we observed that the HyperAST outperforms it with an order-of-magnitude difference from ×6 up to ×8076 in CPU construction time and from ×12 up to ×1159 in memory footprint. While the HyperAST requires up to 2 h 22 min and 7.2 GB for the biggest project, Spoon requires up to 93 h and 31 min and 2.2 TB. The gains in construction time varied from 83.4 % to 99.99 % and the gains in memory footprint varied from 91.8 % to 99.9 %. We further compared the task of finding references of declarations with the HyperAST and Spoon. We observed on average 90 % precision and 97 % recall without a significant difference in search time.
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Dates and versions

hal-03764541 , version 1 (30-08-2022)

Identifiers

  • HAL Id : hal-03764541 , version 1

Cite

Quentin Le Dilavrec, Djamel Eddine Khelladi, Arnaud Blouin, Jean-Marc Jézéquel. HyperAST: Enabling Efficient Analysis of Software Histories at Scale. ASE 2022 - 37th IEEE/ACM International Conference on Automated Software Engineering, Oct 2022, Oakland, United States. pp.1-12. ⟨hal-03764541⟩
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