Effective race detection for event-driven programs

Raychev, Veselin; Vechev, Martin; Sridharan, Manu

doi:10.1145/2509136.2509538

Cited by 112 publications

(93 citation statements)

References 17 publications

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“…We-bRacer [18] and EventRacer [20] are two recent studies focusing on detecting races for one type of event-based programs: web applications. A web application is typically executed by the browser in a single thread in an event-driven style.…”

Section: Race Detection For Event-based Programsmentioning

confidence: 99%

“…Naively applying these tools for event-driven mobile systems works poorly, because they implicitly assume that events handled in one thread are ordered by the program order. Recent work proposed a causality model for detecting races in the event-driven JavaScript applications [18,20]. However, a causality model that accounts for the unique causal relations in a mobile system, and a race detector based on it has been lacking.…”

Section: Introductionmentioning

confidence: 99%

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Race detection for event-driven mobile applications

Hsiao

Narayanasamy

et al. 2014

SIGPLAN Not.

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Mobile systems commonly support an event-based model of concurrent programming. This model, used in popular platforms such as Android, naturally supports mobile devices that have a rich array of sensors and user input modalities. Unfortunately, most existing tools for detecting concurrency errors of parallel programs focus on a thread-based model of concurrency. If one applies such tools directly to an event-based program, they work poorly because they infer false dependencies between unrelated events handled sequentially by the same thread.In this paper we present a race detection tool named CAFA for event-driven mobile systems. CAFA uses the causality model that we have developed for the Android event-driven system. A novel contribution of our model is that it accounts for the causal order due to the event queues, which are not accounted for in past data race detectors. Detecting races based on low-level races between memory accesses leads to a large number of false positives. CAFA overcomes this problem by checking for races between high-level operations. We discuss our experience in using CAFA for finding and understanding a number of known and unknown harmful races in open-source Android applications.

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Section: Race Detection For Event-based Programsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Race detection for event-driven mobile applications

Hsiao

Narayanasamy

et al. 2014

SIGPLAN Not.

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“…In the absence of an ordering, the execution order of the asynchronous tasks (and consequently, the conflicting operations) is non-deterministic, and we would have a data race. Data races of this form in single-threaded event-driven programs were identified for client-side web applications in [9,20,24,30].…”

Section: Data Races and Happens-before Reasoningmentioning

confidence: 99%

“…The rules for single-threaded event-driven programs (e.g., [24]) can be obtained by specializing the thread-local rules. Dropping the FIFO rule gives the non-deterministic scheduling semantics of asynchronous programs (e.g., [8]).…”

Section: Happens-before Relationmentioning

confidence: 99%

“…Race detection for multi-threaded programs is a well-researched topic (e.g., [18,21,22,25,28]). Recently, race detection for single-threaded eventdriven programs (also called asynchronous programs) is studied in the context of client-side web applications (e.g., [20,24,30]). Unfortunately, race detection for Android applications requires reasoning about both thread interleavings and event dispatch; ignoring one or the other leads to false positives.…”

Section: Introductionmentioning

confidence: 99%

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Race detection for Android applications

2014

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Programming environments for smartphones expose a concurrency model that combines multi-threading and asynchronous eventbased dispatch. While this enables the development of efficient and feature-rich applications, unforeseen thread interleavings coupled with non-deterministic reorderings of asynchronous tasks can lead to subtle concurrency errors in the applications.In this paper, we formalize the concurrency semantics of the Android programming model. We further define the happens-before relation for Android applications, and develop a dynamic race detection technique based on this relation. Our relation generalizes the so far independently studied happens-before relations for multithreaded programs and single-threaded event-driven programs. Additionally, our race detection technique uses a model of the Android runtime environment to reduce false positives.We have implemented a tool called DROIDRACER. It generates execution traces by systematically testing Android applications and detects data races by computing the happens-before relation on the traces. We analyzed 15 Android applications including popular applications such as Facebook, Twitter and K-9 Mail. Our results indicate that data races are prevalent in Android applications, and that DROIDRACER is an effective tool to identify data races.

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TimelyRep: Timing deterministic replay for Android web applications

Liu

Yan

Xia

et al. 2020

Software Testing Verif & Rel

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SummaryWith the constantly growing and changing requirements of app users, web techniques are used in mobile application development for better cross‐platform compatibility and online update. As the embedded web contents gain complexity, debugging web apps become a critical demand. Web replay tools can record program inputs and reproduce the same execution for debugging and performance tuning. However, traditional replay approaches are largely intended for apps with desktop interaction methods (keyboard, mouse) and require modification to the browser, which limits their applicability in mobile platforms.In this paper, we develop TimelyRep, which provides deterministic record‐and‐replay as a software library, running on commodity Android. TimelyRep can be used for app development with unmodified Android devices and for production to collect faulty execution from users. Also, we propose an efficient replay timing control mechanism and achieve higher timing precision as facing higher event rate on touchscreen devices. TimelyRep also supports cross‐device replay and can replay logged event traces on different devices, which is useful for developers to reproduce user inputs on their own devices. We evaluate TimelyRep with real‐world web applications. The results show that TimelyRep is useful for recreating program bugs and maintaining low delays for touch‐intensive web games.

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Effective race detection for event-driven programs

Cited by 112 publications

References 17 publications

Race detection for event-driven mobile applications

Race detection for event-driven mobile applications

Race detection for Android applications

TimelyRep: Timing deterministic replay for Android web applications

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