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Learning analysis strategies for octagon and context sensitivity from labeled data generated by static analyses

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Abstract

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 33\(\times \) 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%. The general idea behind our methodis applicable to other types of static analyses as well. We demonstrate that our method is also effective to learn a strategy for context-sensitivity of interval analysis.

Keywords

Static analysis Machine learning Relational analysis Context-sensitivity 

Notes

Acknowledgements

This work was supported by Samsung Research Funding & Incubation Center of Samsung Electronics under Project Numbers SRFC-IT1701-09. This work was supported by Institute for Information & Communications Technology Promotion (IITP) grant funded by the Korea government (MSIT) (No. 2015-0-00565, Development of Vulnerability Discovery Technologies for IoT Software Security, No. 2017-0-00184, Self-Learning Cyber Immune Technology Development).

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© Springer Science+Business Media, LLC, part of Springer Nature 2017

Authors and Affiliations

  1. 1.University of PennsylvaniaPhiladelphiaUSA
  2. 2.Korea UniversitySeoulKorea
  3. 3.KAISTDaejeonKorea

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