Study and Refactoring of Android Asynchronous Programming (T)

Lin, Yu Feng; Okur, Semih; Dig, Danny

doi:10.1109/ase.2015.50

Cited by 51 publications

(29 citation statements)

References 26 publications

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“…Lin et al developed a tool for automatically refactoring off Android apps to include AyncTasks . Using AsyncTasks makes the application more responsive, but they might generate memory leaks.…”

Section: Related Workmentioning

confidence: 99%

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Practical static analysis of context leaks in Android applications

2018

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Summary Android native applications, written in Java and distributed in APK format, are widely used in mobile devices. Their specific pattern of use lets the operating system control the creation and destruction of resources, such as activities and services (contexts). Programmers are not supposed to interfere with such life cycle events. Otherwise, contexts might be leaked, ie, they will never be deallocated from memory, or be deallocated late, leading to memory exhaustion and frozen applications. In practice, it is easy to write incorrect code, which hinders garbage collection of contexts and leads to context leakages. In this work, we present a novel static analysis method that finds context leaks in Android code. We apply this analysis to APKs translated into Java bytecode. We provide a formal analysis of our algorithms and suggest further research directions for improving precision by combining different approaches. We discuss the results of a large number of experiments with our analysis, which reveal context leaks in many widely used applications from the Android marketplace. This shows the practical usefulness of our technique and its superiority w.r.t. the well‐known Lint and Infer static analysis tools. We estimate the amount of memory saved by the collection of the leaks found and explain, experimentally, where programmers often go wrong and limitations of our tool. Such lessons could be used for designing of a sound or more powerful static analysis tool. This work can be considered as a practical application of software analysis techniques to solve practical problems.

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Section: Related Workmentioning

confidence: 99%

“…Lin et al developed a tool for automatically refactoring off Android apps to include AyncTasks. 48 Using AsyncTasks makes the application more responsive, but they might generate memory leaks. In the paper, the authors proposed an automatically refactoring to transform misused AsyncTask by merging good coding patterns.…”

Section: Related Workmentioning

confidence: 99%

Practical static analysis of context leaks in Android applications

2018

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“…Among 1019 apps, 2968 AsyncTasks, 1248 Threads, 286 In-tentServices, and 35 AsyncTaskLoaders are used. AsyncTask and Thread account for the majority, which also conforms to prior work [53,54]. Q2: Are there any basic rules implied by the single-GUI-thread model that Android developers should obey in async programming?…”

Section: Formative Studymentioning

confidence: 54%

Efficiently manifesting asynchronous programming errors in Android apps

Fan

Sus

Chen

et al. 2018

Proceedings of the 33rd ACM/IEEE International Conference on Automated Software Engineering

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Android, the #1 mobile app framework, enforces the single-GUIthread model, in which a single UI thread manages GUI rendering and event dispatching. Due to this model, it is vital to avoid blocking the UI thread for responsiveness. One common practice is to offload long-running tasks into async threads. To achieve this, Android provides various async programming constructs, and leaves developers themselves to obey the rules implied by the model. However, as our study reveals, more than 25% apps violate these rules and introduce hard-to-detect, fail-stop errors, which we term as aysnc programming errors (APEs). To this end, this paper introduces APEChecker, a technique to automatically and efficiently manifest APEs. The key idea is to characterize APEs as specific fault patterns, and synergistically combine static analysis and dynamic UI exploration to detect and verify such errors. Among the 40 real-world Android apps, APEChecker unveils and processes 61 APEs, of which 51 are confirmed (83.6% hit rate). Specifically, APEChecker detects 3X more APEs than the state-of-art testing tools (Monkey, Sapienz and Stoat), and reduces testing time from half an hour to a few minutes. On a specific type of APEs, APEChecker confirms 5X more errors than the data race detection tool, EventRacer, with very few false alarms. CCS CONCEPTS• Software and its engineering → Software testing and debugging;such fatal programming errors that violate the rules implied by the single-UI-thread model as async programming errors (APEs).Such bugs in Android are not easy to detect manually, due to (1) they usually reside in the code of handling interactions between UI thread and async threads, which can be rather complicated for manual analysis; (2) they can only be triggered at the right states of GUI components (e.g., activity, fragment) with complicated lifecycle [21,30]; (3) they have to be triggered at right thread scheduling, while the execution time of async threads is affected by the task and its running environment (e.g., network stability, system load).Even worse, existing bug detection techniques are ineffective for such bugs. First, most GUI testing techniques, e.g., random testing [39,57], search-based testing [58,60], and model-based testing [2,3,8,78,85], are designed for functional testing in general. They aim at enumerating all possible event sequences (GUI-level events in particular) to manifest bugs, which is unscalable and time-consuming. Additionally, they mainly aim at improving code coverage, which may not be sufficient for exhibiting APEs -require specific event sequences with appropriate lifecycle states and thread scheduling. Second, static analysis tools, e.g., Lint [35], Find-Bugs [19] and PMD [67], although scalable, only enforce simple rules (syntax or trivial control/data-flow analysis) to locate suspicious bugs. For example, Lint declares it can find "WrongThread" errors (one type of APEs) [24]. However, as our evaluation in Section 5 demonstrates, Lint incurs a number of false negatives -failing to detect those so...

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“…The consequence is that the service is never destroyed and the WiFi lock acquired at line 15 is never released. The bug is fixed by calling stopService or stopSelf to stop the execution of the service (see line 18).…”

Section: A the Challengementioning

confidence: 99%

“…Control flow information of callbacks is also useful for verifying code transformations such as automatic refactoring techniques. There has been research which shows that refactoring of Android asynchronous constructs involve multiple related callbacks [18].…”

mentioning

confidence: 99%

Generating Predicate Callback Summaries for the Android Framework

Perez

2017

2017 IEEE/ACM 4th International Conference on Mobile Software Engineering and Systems (MOBILESoft)

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Abstract-One of the challenges of analyzing, testing and debugging Android apps is that the potential execution orders of callbacks are missing from the apps' source code. However, bugs, vulnerabilities and refactoring transformations have been found to be related to callback sequences. Existing work on control flow analysis of Android apps have mainly focused on analyzing GUI events. GUI events, although being a key part of determining control flow of Android apps, do not offer a complete picture. Our observation is that orthogonal to GUI events, the Android API calls also play an important role in determining the order of callbacks. In the past, such control flow information has been modeled manually. This paper presents a complementary solution of constructing program paths for Android apps. We proposed a specification technique, called Predicate Callback Summary (PCS), that represents the callback control flow information (including callback sequences as well as the conditions under which the callbacks are invoked) in Android API methods and developed static analysis techniques to automatically compute and apply such summaries to construct apps' callback sequences. Our experiments show that by applying PCSs, we are able to construct Android apps' control flow graphs, including inter-callback relations, and also to detect infeasible paths involving multiple callbacks. Such control flow information can help program analysis and testing tools to report more precise results. Our detailed experimental data is available at: goo.gl/NBPrKs

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Study and Refactoring of Android Asynchronous Programming (T)

Cited by 51 publications

References 26 publications

Practical static analysis of context leaks in Android applications

Practical static analysis of context leaks in Android applications

Efficiently manifesting asynchronous programming errors in Android apps

Generating Predicate Callback Summaries for the Android Framework

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