Mobile apps bring unprecedented levels of convenience, yet they are often buggy, and their bugs offset the convenience the apps bring. A key reason for buggy apps is that they must handle a vast variety of system and user actions such as being randomly killed by the OS to save resources, but app developers, facing tough competitions, lack time to thoroughly test these actions. AppDoctor is a system for efficiently and effectively testing apps against many system and user actions, and helping developers diagnose the resultant bug reports. It quickly screens for potential bugs using approximate execution, which runs much faster than real execution and exposes bugs but may cause false positives. From the reports, AppDoctor automatically verifies most bugs and prunes most false positives, greatly saving manual inspection effort. It uses action slicing to further speed up bug diagnosis. We implement AppDoctor in Android. It operates as a cloud of physical devices or emulators to scale up testing. Evaluation on 53 out of 100 most popular apps in Google Play and 11 of the most popular open-source apps shows that, AppDoctor effectively detects 72 bugs-including two bugs in the Android framework that affect all apps-with quick checking sessions, speeds up testing by 13.3 times, and vastly reduces diagnosis effort.

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We present POS, a concurrency testing approach that samples the partial order of concurrent programs. POS uses a novel prioritybased scheduling algorithm that dynamically reassigns priorities regarding the partial order information and formally ensures that each partial order will be explored with significant probability. POS is simple to implement and provides a probabilistic guarantee of error detection better than state-of-the-art sampling approaches. Evaluations show that POS is effective in covering the partial-order space of micro-benchmarks and finding concurrency bugs in real-world programs, such as Firefox's JavaScript engine SpiderMonkey.

Despite their wide deployment, distributed systems remain notoriously hard to reason about. Unexpected interleavings of concurrent operations and failures may lead to undefined behaviors and cause serious consequences. We present Morpheus, the first concurrency testing tool leveraging partial order sampling, a randomized testing method formally analyzed and empirically validated to provide strong probabilistic guarantees of error-detection, for real-world distributed systems. Morpheus introduces conflict analysis to further improve randomized testing by predicting and focusing on operations that affect the testing result. Inspired by the recent shift in building distributed systems using higher-level languages and frameworks, Morpheus targets Erlang. Evaluation on four popular distributed systems in Erlang including RabbitMQ, a message broker service, and Mnesia, a distributed database in the Erlang standard libraries, shows that Morpheus is effective: It found previously unknown errors in every system checked, 11 total, all of which are flaws in their core protocols that may cause deadlocks, unexpected crashes, or inconsistent states.CCS Concepts • Software and its engineering → Software testing and debugging; • Theory of computation → Distributed computing models.

This paper describes a new optimistic concurrency control algorithm for tree-structured data called meld. Each transaction executes on a snapshot of a multiversion database and logs a record with its intended updates. Meld processes log records in log order on a cached partial-copy of the last committed state to determine whether each transaction commits. If so, it merges the transaction's updates into that state. Meld is used in the Hyder transaction system and enables Hyder to scale out without partitioning. Since meld is on the critical path of transaction execution, it must be very fast. The paper describes the meld algorithm in detail and reports on an evaluation of an implementation. It can perform over 400K update transactions per second for transactions with two operations, and 130K for transactions with eight operations.