Automated test generation for detection of leaks in Android applications

Zhang, Hailong; Wu, Haowei; Rountev, Atanas

doi:10.1145/2896921.2896932

Cited by 44 publications

(29 citation statements)

References 26 publications

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“…The literature includes several contributions on highlighting issues related to the performance of Android apps such as poor responsiveness [29] and exception handling [55]. Yang et al [74], for example, have proposed a systematic testing approach to uncover and quantify common causes of poor [27] MAMBA [28] Pretect [29] SSDA [30] TrimDroid [8] ERVA [9] SAPIENZ [11] RacerDroid [31] DiagDroid [32] MOTIF [33] DRUN [34] GAT [35] Zhang et al [36] Jabbarvand et al [37] Qian et al [38] Ermuth et al [39] Zhang et al [40] Zhang et al [41] dLens [42] Packeviius et al [43] Knorr et al [44] IntentDroid [45] Farto et al [46] Bielik et al [47] MobiGUITAR [48] Aktouf et al [49] AppAudit [50] Hassanshahi et al [51] iMPAcT [52] Deng et al [53] Espada et al [54] Zhang et al [55] QUANTUM [56] CRAXDroid [57] IntentFuzzer [58] Vikomir et al [59] Shahriar et al [60] APSET [61] DROIDRACER [62] AppACTS [63] CAFA [64] Guo et al…”

Section: Test Objectivesmentioning

confidence: 99%

“…It re-executes all the unit test cases every time when a piece of code is changed. As an example, Hu et al [84] have applied unit testing to [26] CRASHSCOPE [27] MAMBA [28] Pretect [29] DroidMate [93] SSDA [30] TrimDroid [8] ERVA [9] Clapp et al [10] SAPIENZ [11] RacerDroid [31] Baek et al [94] DiagDroid [32] MobiPlay [95] MOTIF [33] DRUN [34] DroidDEV [96] GAT [35] Zhang et al [36] Jabbarvand et al [37] Qian et al [38] Ermuth et al [39] Cadage [97] Zhang et al [40] Zhang et al [41] dLens [42] Sonny et al [98] Packeviius et al [43] SIG-Droid [99] Knorr et al [44] TAST [100] IntentDroid [45] Griebe et al [101] Farto et al [46] Bielik et al [47] MobiGUITAR [48] AGRippin [102] Aktouf et al [49] THOR [103] AppAudit [50] Morgado et al [104] Hassanshahi et al [51] iMPAcT [52] Deng et al [53] Espada et al…”

Section: Which Test Levels Are Addressed?mentioning

confidence: 99%

“…Table 5 summarises the aforementioned test phases, where the most recurrently applied testing phase is system testing (accounting for nearly 80% of the selected publications), followed by unit testing and integration testing, respectively. [26] CRASHSCOPE [27] MAMBA [28] Pretect [29] DroidMate [93] SSDA [30] TrimDroid [8] ERVA [9] Clapp et al [10] SAPIENZ [11] RacerDroid [31] Baek et al [94] DiagDroid [32] MobiPlay [95] MOTIF [33] DRUN [34] DroidDEV [96] GAT [35] Zhang et al [36] Jabbarvand et al [37] Qian et al [38] Ermuth et al [39] Cadage [97] Zhang et al [40] Zhang et al [41] dLens [42] Sonny et al [98] Packeviius et al…”

Section: Which Test Levels Are Addressed?mentioning

confidence: 99%

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Automated Testing of Android Apps: A Systematic Literature Review

et al. 2019

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Automated testing of Android apps is essential for app users, app developers and market maintainer communities alike. Given the widespread adoption of Android and the specificities of its development model, the literature has proposed various testing approaches for ensuring that not only functional requirements but also non-functional requirements are satisfied. In this paper, we aim at providing a clear overview of the state-of-the-art works around the topic of Android app testing, in an attempt to highlight the main trends, pinpoint the main methodologies applied and enumerate the challenges faced by the Android testing approaches as well as the directions where the community effort is still needed. To this end, we conduct a Systematic Literature Review (SLR) during which we eventually identified 103 relevant research papers published in leading conferences and journals until 2016. Our thorough examination of the relevant literature has led to several findings and highlighted the challenges that Android testing researchers should strive to address in the future. After that, we further propose a few concrete research directions where testing approaches are needed to solve recurrent issues in app updates, continuous increases of app sizes, as well as the Android ecosystem fragmentation. 1 INTRODUCTION Android smart devices have become pervasive after gaining tremendous popularity in recent years. As of July 2017, Google Play, the official app store, is distributing over 3 million Android applications (i.e., apps), covering over 30 categories ranging from entertainment and personalisation apps to education and financial apps. Such popularity among developer communities can be attributed to the accessible development environment based on familiar Java programming language as well as the availability of libraries implementing diverse functionalities [1]. The app distribution ecosystem around the official store and other alternative stores such as Anzhi and AppChina is further attractive for users to find apps and organisations to market their apps [2]. Unfortunately, the distribution ecosystem of Android is porous to poorly-tested apps [3]-[5]. Yet, as reported • ξ The corresponding author.

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Section: Test Objectivesmentioning

confidence: 99%

Section: Which Test Levels Are Addressed?mentioning

confidence: 99%

Section: Which Test Levels Are Addressed?mentioning

confidence: 99%

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Automated Testing of Android Apps: A Systematic Literature Review

et al. 2019

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“…Model-based testing is also used to detect some specific bugs in Android apps, for example, Zhang et.al [24] proposed a model-based test generation approach to expose resource leak defects in Android apps. They construct the WTG model proposed by Yang et al [22] to describe the AUT and try to generate test cases for two important categories of neutral sequences, based on common leak patterns specific to Android.…”

Section: Related Workmentioning

confidence: 99%

Widget-Sensitive and Back-Stack-Aware GUI Exploration for Testing Android Apps

Yan¹,

Wu²,

Yan³

et al. 2017

2017 IEEE International Conference on Software Quality, Reliability and Security (QRS)

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Testing is a commonly used approach to ensure the quality of software, of which model-based testing is a hot topic to test GUI programs such as Android applications (apps). Existing approaches mainly either dynamically construct a model that only contains the GUI information, or build a model in the view of code that may fail to describe the changes of GUI widgets during runtime. Besides, most of these models do not support back stack that is a particular mechanism of Android. Therefore, this paper proposes a model LATTE that is constructed dynamically with consideration of the view information in the widgets as well as the back stack, to describe the transition between GUI widgets. We also propose a label set to link the elements of the LATTE model to program snippets. The user can define a subset of the label set as a target for the testing requirements that need to cover some specific parts of the code. To avoid the state explosion problem during model construction, we introduce a definition "state similarity" to balance the model accuracy and analysis cost. Based on this model, a target directed test generation method is presented to generate event sequences to effectively cover the target. The experiments on several real-world apps indicate that the generated test cases based on LATTE can reach a high coverage, and with the model we can generate the event sequences to cover a given target with short event sequences.

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“…However, these tools don't have the ability to provide the life cycle and access information for Java objects. Other researchers have implemented tools to profile Android system and applications in IO overhead [7] , privacy leak [1] , application behaviour [14] , resource leak [19] , energy consumption [3] , etc. There are many researches and tools in other platform to track information of Java objects [2,4,6,8,9,10,13,16,17] .…”

Section: Introductionmentioning

confidence: 99%

YA-JOP:Yet Another Java Object Profiler On Art Virtual Machine

Wang¹,

Shi²,

Li³

2017

Proceedings of the 2017 2nd Joint International Information Technology, Mechanical and Electronic Engineering Conference (JIMEC

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In this paper, we design and implement an efficient and low overhead Java object profiler called YA-JOP on Android 6.0 and its ART virtual machine, which uses an AOT(ahead-oftime) compiler. YA-JOP records the allocation site, the class information, the object size, the birth time and death time, the last access time and the access regular pattern for every Java object. The data profiled can help the developers to detect memory leaks, find reusable objects, implement optimizations like pretenuring, etc. The profiler proposed in this paper has reasonable execution time overhead, imposes no overhead on the Java heap and does not modify any existing key data structure of the ART Virtual Machine, including the object layouts, class layouts and any others.

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Automated test generation for detection of leaks in Android applications

Cited by 44 publications

References 26 publications

Automated Testing of Android Apps: A Systematic Literature Review

Automated Testing of Android Apps: A Systematic Literature Review

Widget-Sensitive and Back-Stack-Aware GUI Exploration for Testing Android Apps

YA-JOP:Yet Another Java Object Profiler On Art Virtual Machine

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