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A Deep Reinforcement Learning-Based Approach for android gui testing 6th

A Deep Reinforcement Learning-Based Approach for Android GUI...

6th Asia Pacific Web (APWeb) and Web-Age Information Management (WAIM) Joint International Conference on Web and Big Data (APWeb-WAIM)

作者： Gao, Yuemeng Tao, Chuanqi Guo, Hongjing Gao, Jerry Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol Nanjing Peoples R China Nanjing Univ Aeronaut & Astronaut Minist Key Lab Safety Crit Software Dev & Verific Nanjing Peoples R China Nanjing Univ Collaborat Innovat Ctr Novel Software Technol & I Nanjing Peoples R China Nanjing Univ State Key Lab Novel Software Technol Nanjing Peoples R China San Jose State Univ Comp Engn Dept San Jose CA 95192 USA

ISBN: (纸本)9783031252006;9783031252013

The mobile application market is booming, and android applications occupy a vast market share. However, the applications may contain many errors. The task in the testing phase is to find these errors as soon as possible. It is urgent to test the application rapidly and effectively. Otherwise, it may affect user experience and cause substantial economic losses. Mobile applications iterate continuously to consummate performance and functional requirements, which leads to the increased complexity of applications and explosive growth of state combinations. Reinforcement learning aims to learn strategies to achieve specific goals by maximizing rewards. This paper applies it to android gui testing. In this paper, we propose ATAC. It is black-box based and adopts Advantage Actor-Critic (A2C) algorithm, which contains an actor (policy) and a critic (value function) to generate test cases automatically through deep reinforcement learning. To verify the validity of the proposed approach, we conducted our experiment on seventeen open-source applications from Github. Compared with ARES and Monkey, ATAC shows higher code coverage and detects more errors.

关键词： Reinforement learning android gui testing Advantage actor critic

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Automata-Based Trace Analysis for Aiding Diagnosing gui testing Tools for android 2023

Automata-Based Trace Analysis for Aiding Diagnosing GUI Test...

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31st ACM Joint Meeting of the European Software Engineering Conference / Symposium on the Foundations-of-Software-Engineering (ESEC/FSE)

作者： Ma, Enze Huang, Shan He, Weigang Su, Ting Wang, Jue Liu, Huiyu Pu, Geguang Su, Zhendong East China Normal Univ Shanghai Key Lab Trustworthy Comp Inst Software Engn Shanghai Peoples R China Nanjing Univ State Key Lab Novel Software Tech Nanjing Peoples R China Nanjing Univ Dept Comp Sci & Tech Nanjing Peoples R China Swiss Fed Inst Technol Dept Comp Sci Zurich Switzerland

ISBN: (纸本)9798400703270

Benchmarking software testing tools against known bugs is a classic approach to evaluating the tools' bug finding abilities. However, this approach is difficult to give some clues on the tool-missed bugs to aid diagnosing the testing tools. As a result, heavy and ad hoc manual analysis is needed. In this work, in the setting of gui testing for android apps, we introduce an automata-based trace analysis approach to tackling the key challenge of manual analysis, i.e., how to analyze the lengthy event traces generated by a testing tool against a missed bug to find the clues. Our key idea is that, we model a bug in the form of a finite automaton which captures its bug-triggering traces;and match the event traces generated by the testing tool (which misses this bug) against this automaton to obtain the clues. Specifically, the clues are presented in the form of three designated automata-based coverage values. We apply our approach to enhance Themis, a representative benchmark suite for android, to aid diagnosing gui testing tools. Our extensive evaluation on nine state-of-the-art gui testing tools and the involvement with several tool developers shows that our approach is feasible and useful. Our approach enables Themis+ (the enhanced benchmark suite) to provide the clues on the tool-missed bugs, and all the Themis+'s clues are identical or useful, compared to the manual analysis results of tool developers. Moreover, the clues have helped find several tool weaknesses, which were unknown or unclear before. Based on the clues, two actively-developing industrial testing tools in our study have quickly made several optimizations and demonstrated their improved bug finding abilities. All the tool developers give positive feedback on the usefulness and usability of Themis+'s clues. Themis+ is available at https://***/DDroid-android/home.

关键词： android gui testing Runtime Verification Trace Analysis

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Enhancing gui Exploration Coverage of android Apps with Deep Link-Integrated Monkey

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ACM TRANSACTIONS ON SOFTWARE ENGINEERING AND METHODOLOGY 2024年第6期33卷 1-31页

作者： Hu, Han Wang, Han Dong, Ruiqi Chen, Xiao Chen, Chunyang Monash Univ Fac Informat Technol Clayton Vic Australia Swinburne Univ Technol Fac Sci Engn & Technol Hawthorn Vic Australia Univ Newcastle Callaghan NSW Australia Tech Univ Munich Munich Germany

Mobile apps are ubiquitous in our daily lives for supporting different tasks such as reading and chatting. Despite the availability of many gui testing tools, app testers still struggle with low testing code coverage due to tools frequently getting stuck in loops or overlooking activities with concealed entries. This results in a significant amount of testing time being spent on redundant and repetitive exploration of a few gui pages. To address this, we utilize android's deep links, which assist in triggering android intents to lead users to specific pages and introduce a deep link-enhanced explorationmethod. This approach, integrated into the testing tool Monkey, gives rise to Delm (Deep Link-enhanced Monkey). Delm oversees the dynamic exploration process, guiding the tool out of meaningless testing loops to unexplored gui pages. We provide a rigorous activity context mock-up approach for triggering existing android intents to discover more activities with hidden entrances. We conduct experiments to evaluate Delm's effectiveness on activity context mock-up, activity coverage, method coverage, and crash detection. The findings reveal that Delm can mock up more complex activity contexts and significantly outperform state-of-the-art baselines with 27.2% activity coverage, 21.13% method coverage, and 23.81% crash detection.

关键词： gui android gui testing android app exploration app analysis

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Understanding the Reproducibility Issues of Monkey for gui testing 9th

Understanding the Reproducibility Issues of Monkey for GU...

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9th International Symposium on Dependable Software Engineering: Theories, Tools and Applications, SETTA 2023

作者： Liu, Huiyu Kong, Qichao Wang, Jue Su, Ting Sun, Haiying Shanghai Key Laboratory of Trustworthy Computing East China Normal University Shanghai China Nanjing University Nanjing China

ISBN: (纸本)9789819986637

Automated gui testing is an essential activity in developing android apps. Monkey is a widely used representative automated input generation (AIG) tool to efficiently and effectively detect crash bugs in android apps. However, it faces challenges in reproducing the crash bugs it detects. To deeply understand the symptoms and root causes of these challenges, we conducted a comprehensive study on the reproducibility issues of Monkey with android apps. We focused on Monkey’s capability to reproduce crash bugs using its built-in replay functionality and explored the root causes of its failures. Specifically, we selected six popular open-source apps and conducted automated instrumentation on them to monitor the invocations of event handlers within the apps. Subsequently, we performed gui testing with Monkey on these instrumented apps for 6,000 test cases and collected 56 unique crash bugs. For each bug, we replayed it 200 times using Monkey’s replay function and calculated the success rate. Through manual analysis of screen recording files, log files of event handlers, and the source code of the apps, we pinpointed five root causes contributing to Monkey’s reproducibility issues: Injection Failure, Event Ambiguity, Data Loading, Widget Loading, and Dynamic Content. Our research showed that only 36.6% of the replays successfully reproduced the crash bugs, shedding light on Monkey’s limitations in consistently reproducing detected crash bugs. Additionally, we delved deep into the unsuccessfully reproduced replays to discern the root causes behind the reproducibility issues and offered insights for developing future AIG tools. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： android gui testing Empirical Study Reproducibility

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Crowdsourced Requirements Generation for Automatic testing via Knowledge Graph 2020

Crowdsourced Requirements Generation for Automatic Testing v...

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29th ACM SIGSOFT International Symposium on Software testing and Analysis (ISSTA)

作者： Guo, Chao He, Tieke Yuan, Wei Guo, Yue Hao, Rui Nanjing Univ State Key Lab Novel Software Technol Nanjing Peoples R China

ISBN: (纸本)9781450380089

Crowdsourced testing provides an effective way to deal with the problem of android system fragmentation, as well as the application scenario diversity faced by android testing. The generation of test requirements is a significant part of crowdsourced testing. However, manually generating crowdsourced testing requirements is tedious, which requires the issuers to have the domain knowledge of the android application under test. To solve these problems, we have developed a tool named KARA, short for Knowledge Graph Aided Crowdsourced Requirements Generation for android testing. KARA first analyzes the result of automatic testing on the android application, through which the operation sequences can be obtained. Then, the knowledge graph of the target application is constructed in a manner of pay-as-you-go. Finally, KARA utilizes knowledge graph and the automatic testing result to generate crowdsourced testing requirements with domain knowledge. Experiments prove that the test requirements generated by KARA are well understandable, and KARA can improve the quality of crowdsourced testing. The demo video can be found at https://***/kE-dOiekWWM.

关键词： android gui testing Crowdsourced Requirements Knowledge Graph

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A Crawling Approach of Hierarchical gui Model Generation for android Applications

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JOURNAL OF INTERNET TECHNOLOGY 2018年第5期19卷 1613-1623页

作者： Liu, Chien-Hung Chen, Ping-Hung Natl Taipei Univ Technol Dept Comp Sci & Informat Engn Taipei Taiwan

As the number of android applications has increased dramatically, there is a rising concern about their quality and reliability. In particular, the rich gui interactions supported by android should be thoroughly tested in order to ensure if the behavior of an android application is correct. However, manually creating a gui state model can be tedious and error- prone, especially for a nontrivial application. This paper proposes a crawler that can automatically generate the gui state model for an android application. Particularly, a hierarchical state model is employed to represent the intra- and interactivity gui behavior of android applications in order to increase the model readability. Empirical experiments were conducted to evaluate the proposed crawler and the generated model. The results show that the state model generated by the crawler has a promising coverage as compared to the model created manually. The hierarchical state model can greatly improve the model readability to ease the gui behavior analysis and validation for android applications.

关键词： android crawler android gui model android gui testing

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PATS: A Parallel gui testing Framework for android Applications 39

PATS: A Parallel GUI Testing Framework for Android Applicati...

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39th IEEE Annual International Computer Software and Applications Conference Workshops (COMPSAC)

作者： Wen, Hsiang-Lin Lin, Chia-Hui Hsieh, Tzong-Han Yang, Cheng-Zen Yuan Ze Univ Dept Comp Sci & Engn Chungli 320 Taiwan

ISBN: (纸本)9781467365635

android is currently the most widely used operating system for mobile devices. gui testing for android applications becomes an important research area in which many studies have been conducted. The past studies show that testing a complicated gui design may need a large number of test cases which increases exponentially due to the complexity of the gui. Developers then need to spend a large amount of testing time in executing the test cases to explore the potential software defects. Unfortunately, the testing efficiency issue has not been comprehensively discussed in related studies. In this paper, we describe a parallel gui testing platform called PATS (Parallel android testing System) which performs gui testing based on a master-slave model. In PATS, the application under test is analyzed dynamically under the cooperation of the master and the slaves. Since the test cases are also generated in parallel at the runtime, the testing efficiency can be improved. We have implemented a prototype and conducted experiments with android apps. The experimental results show that PATS can effectively improve the testing time with 18.87-35.78% performance improvements.

关键词： android gui testing parallel testing gui ripping fine-grained event sequences

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