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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.

Information flow control (IFC) provides confidentiality by enforcing noninterference, which ensures that high-secrecy values cannot affect low-secrecy values. Prior work introduces fine-grained IFC approaches that modify the programming language and use non-standard compilation tools, impose run-time overhead, or report false secrecy leaks—all of which hinder adoption. This paper presents Cocoon, a Rust library for static type-based IFC that uses the unmodified Rust language and compiler. The key insight of Cocoon lies in leveraging Rust’s type system and procedural macros to establish an effect system that enforces noninterference. A performance evaluation shows that using Cocoon increases compile time but has no impact on application performance. To demonstrate Cocoon’s utility, we retrofitted two popular Rust programs, the Spotify TUI client and Mozilla’s Servo browser engine, to use Cocoon to enforce limited confidentiality policies