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arXiv · 2404.04621

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

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.

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Chujun Geng, Spyros Blanas, Michael D. Bond, Yang Wang. 2024-04-06. IsoPredict: Dynamic Predictive Analysis for Detecting Unserializable Behaviors in Weakly Isolated Data Store Applications. https://doi.org/10.1145/3656391

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