Filip Niksic
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Synchronization Schemas
Rajeev Alur
Phillip Hilliard
Zachary G. Ives
Konstantinos Kallas
Konstantinos Mamouras
Caleb Stanford
Val Tannen
Anton Xue
PODS'21: Proceedings of the 40th ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database Systems, Virtual Event, China, June 20-25, 2021, ACM, pp. 1-18
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We present a type-theoretic framework for data stream processing for real-time decision making, where the desired computation involves a mix of sequential computation, such as smoothing and detection of peaks and surges, and naturally parallel computation, such as relational operations, key-based partitioning, and map-reduce. Our framework unifies sequential (ordered) and relational (unordered) data models. In particular, we define synchronization schemas as types, and series-parallel streams (SPS) as objects of these types. A synchronization schema imposes a hierarchical structure over relational types that succinctly captures ordering and synchronization requirements among different kinds of data items. Series-parallel streams naturally model objects such as relations, sequences, sequences of relations, sets of streams indexed by key values, time-based and event-based windows, and more complex structures obtained by nesting of these. We introduce series-parallel stream transformers (SPST) as a domain-specific language for modular specification of deterministic transformations over such streams. SPSTs provably specify only monotonic transformations allowing streamability, have a modular structure that can be exploited for correct parallel implementation, and are composable allowing specification of complex queries as a pipeline of transformations.
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Reducing Time-To-Fix For Fuzzer Bugs
Rui Abreu
Hadi Ravanbakhsh
Ramesh Viswanathan
36th IEEE/ACM International Conference on Automated Software Engineering (2021)
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At Google, fuzzing C/C++ libraries has discovered tens of thousands of security and robustness bugs. However, these bugs are often reported much after they were introduced. Developers are provided only with fault-inducing test inputs and replication instructions that highlight a crash, but additional debugging information may be needed to localize the cause of the bug. Hence, developers need to spend substantial time debugging the code and identifying commits that introduced the bug. In this paper, we discuss our experience with automating a fuzzing-enabled bisection that pinpoints the commit in which the crash first manifests itself. This ultimately reduces the time critical bugs stay open in our code base. We report on our experience over the past year, which shows that developers fix bugs on average 2.23 times faster when aided by this automated analysis.
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