ABSTRACT
Event-based races are the main source of concurrency errors in Android apps. Prior approaches for scalable detection of event-based races have been dynamic. Due to their dynamic nature, these approaches suffer from coverage and false negative issues. We introduce a precise and scalable static approach and tool, named SIERRA, for detecting Android event-based races. SIERRA is centered around a new concept of "concurrency action" (that reifies threads, events/messages, system and user actions) and statically-derived order (happens-before relation) between actions. Establishing action order is complicated in Android, and event-based systems in general, because of externally-orchestrated control flow, use of callbacks, asynchronous tasks, and ad-hoc synchronization. We introduce several novel approaches that enable us to infer order relations statically: auto-generated code models which impose order among lifecycle and GUI events; a novel context abstraction for event-driven programs named action-sensitivity and finally, on-demand path sensitivity via backward symbolic execution to further rule out false positives. We have evaluated SIERRA on 194 Android apps. Of these, we chose 20 apps for manual analysis and comparison with a state-of-the-art dynamic race detector. Experimental results show that SIERRA is effective and efficient, typically taking 960 seconds to analyze an app and revealing 43 potential races. Compared with the dynamic race detector, SIERRA discovered an average 29.5 true races with 3.5 false positives, where the dynamic detector only discovered 4 races (hence missing 25.5 races per app) -- this demonstrates the advantage of a precise static approach. We believe that our approach opens the way for precise analysis and static event race detection in other event-driven systems beyond Android.
- Android Developers. 2017. Activity Lifecycle. (2017). http://developer.android.com/reference/android/app/Activity.htmlGoogle Scholar
- Android Developers. 2017. App Components. (2017). https://developer.android.com/guide/components/index.htmlGoogle Scholar
- Steven Arzt, Siegfried Rasthofer, Christian Fritz, Eric Bodden, Alexandre Bartel, Jacques Klein, Yves Le Traon, Damien Octeau, and Patrick McDaniel. 2014. FlowDroid: Precise Context, Flow, Field, Object-sensitive and Lifecycle-aware Taint Analysis for Android Apps. In Proceedings of the 35th ACM SIGPLAN Conference on Programming Language Design and Implementation (PLDI '14). ACM, New York, NY, USA, 259--269. Google ScholarDigital Library
- Tanzirul Azim and Iulian Neamtiu. 2013. Targeted and Depth-first Exploration for Systematic Testing of Android Apps Proceedings of the 2013 ACM SIGPLAN International Conference on Object Oriented Programming Systems Languages & Applications (OOPSLA '13). ACM, New York, NY, USA, 641--660. Google ScholarDigital Library
- Pavol Bielik, Veselin Raychev, and Martin Vechev. 2015. Scalable Race Detection for Android Applications. In Proceedings of the 2015 ACM SIGPLAN International Conference on Object-Oriented Programming, Systems, Languages, and Applications (OOPSLA 2015). ACM, New York, NY, USA, 332--348. Google ScholarDigital Library
- Sam Blackshear, Bor-Yuh Evan Chang, and Manu Sridharan. 2013. Thresher: Precise Refutations for Heap Reachability Proceedings of the 34th ACM SIGPLAN Conference on Programming Language Design and Implementation (PLDI '13). ACM, New York, NY, USA, 275--286. Google ScholarDigital Library
- Sam Blackshear, Bor-Yuh Evan Chang, and Manu Sridharan. 2015 a. Selective Control-flow Abstraction via Jumping. In Proceedings of the 2015 ACM SIGPLAN International Conference on Object-Oriented Programming, Systems, Languages, and Applications (OOPSLA 2015). ACM, New York, NY, USA, 163--182. Google ScholarDigital Library
- Sam Blackshear, Alexandra Gendreau, and Bor-Yuh Evan Chang. 2015 b. Droidel: A General Approach to Android Framework Modeling Proceedings of the 4th ACM SIGPLAN International Workshop on State Of the Art in Program Analysis (SOAP 2015). ACM, New York, NY, USA, 19--25. Google ScholarDigital Library
- Michael D. Bond, Katherine E. Coons, and Kathryn S. McKinley. 2010. PACER: Proportional Detection of Data Races. In Proceedings of the 31st ACM SIGPLAN Conference on Programming Language Design and Implementation (PLDI '10). ACM, New York, NY, USA, 255--268. Google ScholarDigital Library
- Yinzhi Cao, Yanick Fratantonio, Antonio Bianchi, Manuel Egele, Christopher Kruegel, Giovanni Vigna, and Yan Chen. 2015. EdgeMiner: Automatically Detecting Implicit Control Flow Transitions through the Android Framework. In Proceedings of the ISOC Network and Distributed System Security Symposium (NDSS).Google ScholarCross Ref
- Feng Chen, Traian Florin Serbanuta, and Grigore Rosu. 2008. jPredictor: A Predictive Runtime Analysis Tool for Java Proceedings of the 30th International Conference on Software Engineering (ICSE '08). ACM, New York, NY, USA, 221--230. Google ScholarDigital Library
- Leonardo de Moura and Nikolaj Bjørner. 2008. Z3: An Efficient SMT Solver. In Tools and Algorithms for the Construction and Analysis of Systems, 14th International Conference, TACAS 2008, Held as Part of the Joint European Conferences on Theory and Practice of Software, ETAPS 2008, Budapest, Hungary, March 29-April 6, 2008. Proceedings (Lecture Notes in Computer Science), Vol. Vol. 4963. Springer, 337--340. Google ScholarDigital Library
- Isil Dillig, Thomas Dillig, and Alex Aiken. 2011. Precise Reasoning for Programs Using Containers. In Proceedings of the 38th Annual ACM SIGPLAN-SIGACT Symposium on Principles of Programming Languages (POPL '11). ACM, New York, NY, USA, 187--200. Google ScholarDigital Library
- Dawson Engler and Ken Ashcraft. 2003. RacerX: Effective, Static Detection of Race Conditions and Deadlocks Proceedings of the Nineteenth ACM Symposium on Operating Systems Principles (SOSP '03). ACM, New York, NY, USA, 237--252. Google ScholarDigital Library
- Cormac Flanagan and Stephen N. Freund. 2009. FastTrack: Efficient and Precise Dynamic Race Detection Proceedings of the 2009 ACM SIGPLAN Conference on Programming Language Design and Implementation (PLDI '09). Google ScholarDigital Library
- Shin Hong and Moonzoo Kim. 2015. A Survey of Race Bug Detection Techniques for Multithreaded Programmes. Softw. Test. Verif. Reliab. Vol. 25, 3 (May. 2015), 191--217. Google ScholarDigital Library
- Bo Zhou, Iulian Neamtiu, and Rajiv Gupta. 2015. A Cross-platform Analysis of Bugs and Bug-fixing in Open Source Projects: Desktop vs. Android vs. iOS. In 19th International Conference on Evaluation and Assessment in Software Engineering, EASE 2015. 10. Google ScholarDigital Library
- B. Zhou, I. Neamtiu, and R. Gupta. 2015. Experience report: How do bug characteristics differ across severity classes: A multi-platform study. In Software Reliability Engineering (ISSRE), 2015 IEEE 26th International Symposium on. 507--517. Google ScholarDigital Library
Index Terms
- Static Detection of Event-based Races in Android Apps
Recommendations
Static Detection of Event-based Races in Android Apps
ASPLOS '18Event-based races are the main source of concurrency errors in Android apps. Prior approaches for scalable detection of event-based races have been dynamic. Due to their dynamic nature, these approaches suffer from coverage and false negative issues. We ...
Automatically verifying and reproducing event-based races in Android apps
ISSTA 2016: Proceedings of the 25th International Symposium on Software Testing and AnalysisConcurrency has been a perpetual problem in Android apps, mainly due to event-based races. Several event-based race detectors have been proposed, but they produce false positives, cannot reproduce races, and cannot distinguish be- tween benign and ...
Finding resume and restart errors in Android applications
OOPSLA '16Smartphone apps create and handle a large variety of ``instance'' data that has to persist across runs, such as the current navigation route, workout results, antivirus settings, or game state. Due to the nature of the smartphone platform, an app can ...
Comments