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

Abstract

Distributed data stores typically provide weak isolation levels, which are efficient but can lead to unserializable behaviors, which are hard for programmers to understand and often result in errors. 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 to handle divergent application behavior; to solve mutually recursive sets of constraints; and to balance coverage, precision, and performance. An evaluation shows IsoPredict finds unserializable behaviors in four data store benchmarks, and that more than 99% of its predicted executions are feasible.