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Abstract

Distributed systems nowadays are the backbone of computing society, and are expected tohave high availability. Unfortunately, distributed timing bugs, a type of bugs triggered by non-deterministic timing of messages and node crashes, widely exist. They lead to many production-run failures, and are difficult to reason about and patch. Although recently proposed techniques can automatically detect these bugs, how to automatically and correctly x them still remains as an open problem. I designed DFix, a tool that automatically processes distributed timing bug reports, statically analyzes the buggy system, and produces patches. Our evaluation shows that DFix is effective in fixing real-world distributed timing bugs. Concurrency bugs are hard to find, reproduce, and debug. They often escape rigorousin-house testing, but result in large-scale outages in production. Existing concurrency bug detection techniques unfortunately cannot be part of industry's integrated build and test environment due to some open challenges: how to handle code developed by thousands of engineering teams that uses a wide variety of synchronization mechanisms, how to report little/no false positives, and how to avoid excessive testing resource consumption. TSVD is a thread-safety violation detector that addresses these challenges through a new design point in the domain of active testing. Unlike previous techniques that inject delays randomly or employ expensive synchronization analysis, TSVD uses lightweight monitoring of the calling behaviors of thread-unsafe methods, not any synchronization operations, to dynamically identify bug suspects. It then injects corresponding delays to drive the program towards thread-unsafe behaviors, actively learns from its ability or inability to do so, and persists its learning from one test run to the next. TSVD is deployed and regularly used in Microsoft and it has already found over 1000 thread-safety violations from thousands of projects. It detects more bugs than state-of-the-art techniques, mostly with just one test run. Synchronizations are fundamental to the correctness and performance of concurrent software. Unfortunately, correctly identifying all synchronizations has become extremely difficult in modern software systems due to the various types of synchronizations. Previouswork either only infers specific type of synchronization by code analysis or relies on manual effect to annotate the synchronization. SherLock is a tool that uses unsupervised inference to identify synchronizations. SherLock leverages the fact that most synchronizations appear around the conflicting operations and form it into a linear system with a set of synchronization properties and hypotheses. To collect enough observations, SherLock runs the unit tests a small number of times with feedback-based delay injection. I applied SherLock on 8 C# open-source applications. Without any prior knowledge, SherLock inferred 122 unique synchronizations, with few false positives. These inferred synchronizations cover a wide variety of types, including lock operations, fork-join operations, asynchronous operations, framework synchronization, and custom synchronization.

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