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The advent of data center, cloud computing and IoT has thrust distributed systems building into the programming mainstream. Building correct distributed systems is notoriously hard, yet today's developers have little training and few tools to aid them in reasoning about these complex systems. To that end, we present DS2 -a domain-specific language and integrated framework for specifying, synthesizing, and reasoning. The DS2 language is parsimonious, and comes with an operational semantics that lends semantic clarity and enables formal analysis. A variety of techniques for model exploration, active testing, and synthesis of detailed implementations from higher level specifications are being developed. This paper details these aspects of DS2 and provides a roadmap of its evolution.

Recent demand for distributed software had led to a surge in popularity in actorbased frameworks. However, even with the stylized message passing model of actors, writing correct distributed software is still difficult. We present our work on linearizability checking in DS2, an integrated framework for specifying, synthesizing, and testing distributed actor systems. The key insight of our approach is that often subcomponents of distributed actor systems represent common algorithms or data structures (e.g. a distributed hash table or tree) that can be validated against a simple sequential model of the system. This makes it easy for developers to validate their concurrent actor systems without complex specifications. DS2 automatically explores the concurrent schedules that system could arrive at, and it compares observed output of the system to ensure it is equivalent to what the sequential implementation could have produced. We describe DS2's linearizability checking and test it on several concurrent replication algorithms from the literature. We explore in detail how different algorithms for enumerating the model schedule space fare in finding bugs in actor systems, and we present our own refinements on algorithms for exploring actor system schedules that we show are effective in finding bugs.

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