{"id":418130,"date":"2017-07-30T22:16:27","date_gmt":"2017-07-31T05:16:27","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?post_type=msr-research-item&p=418130"},"modified":"2018-10-16T20:00:52","modified_gmt":"2018-10-17T03:00:52","slug":"lasso-detection-using-partial-state-caching","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/lasso-detection-using-partial-state-caching\/","title":{"rendered":"Lasso Detection using Partial-State Caching"},"content":{"rendered":"

We study the problem of finding liveness violations in real-world
\nasynchronous distributed systems. Unlike a safety property, which
\nasserts that certain bad states should never occur during execution, a
\nliveness property states that a program should not remain in a bad
\nstate for an infinitely long period of time. Checking for liveness
\nviolations is an essential testing activity to ensure that a system
\nwill always make progress in production.<\/p>\n

The violation of a liveness property can be demonstrated by a finite
\nexecution where the same system state repeats twice (known as
\nlasso). However, this requires the ability to capture the state
\nprecisely, which is arguably impossible in real-world systems. For
\nthis reason, previous approaches have instead relied on demonstrating
\na long execution where the system remains in a bad state. However,
\nthis hampers debugging because the produced trace can be very long,
\nmaking it hard to understand.<\/p>\n

Our work aims to find liveness violations in real-world systems while
\nstill producing lassos as a bug witness. Our technique relies only on
\npartially caching the system state, which is feasible to achieve
\nefficiently in practice. To make up for imprecision in caching, we use
\nretries: a potential lasso, where the same partial state repeats
\ntwice, is replayed multiple times to gain certainty that the execution
\nis indeed stuck in a bad state.<\/p>\n

We have implemented our technique in the P# programming language and
\nevaluated it on real production systems and several challenging
\nacademic benchmarks.<\/p>\n","protected":false},"excerpt":{"rendered":"

We study the problem of finding liveness violations in real-world asynchronous distributed systems. Unlike a safety property, which asserts that certain bad states should never occur during execution, a liveness property states that a program should not remain in a bad state for an infinitely long period of time. Checking for liveness violations is an 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