Learning a variable-clustering strategy for octagon from labeled data generated by a static analysis

Kihong Heo, Hakjoo Oh, Hongseok Yang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

26 Citations (Scopus)


We present a method for automatically learning an effective strategy for clustering variables for the Octagon analysis from a given codebase. This learned strategy works as a preprocessor of Octagon. Given a program to be analyzed, the strategy is first applied to the program and clusters variables in it. We then run a partial variant of the Octagon analysis that tracks relationships among variables within the same cluster, but not across different clusters. The notable aspect of our learning method is that although the method is based on supervised learning, it does not require manually-labeled data. The method does not ask human to indicate which pairs of program variables in the given codebase should be tracked. Instead it uses the impact pre-analysis for Octagon from our previous work and automatically labels variable pairs in the codebase as positive or negative. We implemented our method on top of a static buffer-overflow detector for C programs and tested it against open source benchmarks. Our experiments show that the partial Octagon analysis with the learned strategy scales up to 100KLOC and is 33x faster than the one with the impact pre-analysis (which itself is significantly faster than the original Octagon analysis), while increasing false alarms by only 2 %.

Original languageEnglish
Title of host publicationStatic Analysis - 23rd International Symposium, SAS 2016, Proceedings
EditorsXavier Rival
PublisherSpringer Verlag
Number of pages20
ISBN (Print)9783662534120
Publication statusPublished - 2016
Event23rd International Symposium on Static Analysis, SAS 2016 - Edinburgh, United Kingdom
Duration: 2016 Sept 82016 Sept 10

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9837 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference23rd International Symposium on Static Analysis, SAS 2016
Country/TerritoryUnited Kingdom

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)


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