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Computer Science > Programming Languages

arXiv:2404.04621 (cs)
[Submitted on 6 Apr 2024]

Title:IsoPredict: Dynamic Predictive Analysis for Detecting Unserializable Behaviors in Weakly Isolated Data Store Applications

Authors:Chujun Geng, Spyros Blanas, Michael D. Bond, Yang Wang
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Abstract:This paper presents the first dynamic predictive analysis for data store applications under weak isolation levels, called Isopredict. Given an observed serializable execution of a data store application, Isopredict generates and solves SMT constraints to find an unserializable execution that is a feasible execution of the application. Isopredict introduces novel techniques that handle divergent application behavior; solve mutually recursive sets of constraints; and balance coverage, precision, and performance. An evaluation on four transactional data store benchmarks shows that Isopredict often predicts unserializable behaviors, 99% of which are feasible.
Subjects: Programming Languages (cs.PL); Databases (cs.DB)
Cite as: arXiv:2404.04621 [cs.PL]
  (or arXiv:2404.04621v1 [cs.PL] for this version)
  https://doi.org/10.48550/arXiv.2404.04621
Journal reference: Proc. ACM Program. Lang., Vol. 8, No. PLDI, Article 161. Publication date: June 2024
Related DOI: https://doi.org/10.1145/3656391

Submission history

From: Michael Bond [view email]
[v1] Sat, 6 Apr 2024 13:02:28 UTC (360 KB)
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