Learning to Boost Disjunctive Static Bug-Finders

Yoonseok Ko, Hakjoo Oh

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

1 Citation (Scopus)


We present a new learning-based approach for accel-erating disjunctive static bug-finders. Industrial static bug-finders usually perform disjunctive analysis, differentiating program states along different execution paths of a program. Such path-sensitivity is essential for reducing false positives but it also increases analysis costs exponentially. Therefore, practical bug-finders use a state-selection heuristic to keep track of a small number of beneficial states only. However, designing a good heuristic for real-world programs is challenging; as a result, modern static bug-finders still suffer from low cost/bug-finding efficiency. In this paper, we aim to address this problem by learning effective state-selection heuristics from data. To this end, we present a novel data-driven technique that efficiently collects alarm-triggering traces, learns multiple candidate models, and adaptively chooses the best model tailored for each target program. We evaluate our approach with Infer and show that our technique significantly improves Infer's bug-finding efficiency for a range of open-source C programs.

Original languageEnglish
Title of host publicationProceedings - 2023 IEEE/ACM 45th International Conference on Software Engineering, ICSE 2023
PublisherIEEE Computer Society
Number of pages13
ISBN (Electronic)9781665457019
Publication statusPublished - 2023
Event45th IEEE/ACM International Conference on Software Engineering, ICSE 2023 - Melbourne, Australia
Duration: 2023 May 152023 May 16

Publication series

NameProceedings - International Conference on Software Engineering
ISSN (Print)0270-5257


Conference45th IEEE/ACM International Conference on Software Engineering, ICSE 2023

Bibliographical note

Publisher Copyright:
© 2023 IEEE.


  • machine learning
  • static analysis

ASJC Scopus subject areas

  • Software


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