Automatic GUI testing for android using reinforcement learning
The developers focus on testing applications, which can be a time-consuming task. To address this issue, we developed AgentDroid, a tool that utilizes reinforcement learning techniques to automate test execution. So far, the results have been impressive, outperforming state-of-the-art RL-based autom...
- Autores:
-
Valbuena Bautista, Daniel
- Tipo de recurso:
- Trabajo de grado de pregrado
- Fecha de publicación:
- 2023
- Institución:
- Universidad de los Andes
- Repositorio:
- Séneca: repositorio Uniandes
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.uniandes.edu.co:1992/64317
- Acceso en línea:
- http://hdl.handle.net/1992/64317
- Palabra clave:
- Reinforcement learning
Testing
Android
Ingeniería
- Rights
- openAccess
- License
- Atribución 4.0 Internacional
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Automatic GUI testing for android using reinforcement learning |
| title |
Automatic GUI testing for android using reinforcement learning |
| spellingShingle |
Automatic GUI testing for android using reinforcement learning Reinforcement learning Testing Android Ingeniería |
| title_short |
Automatic GUI testing for android using reinforcement learning |
| title_full |
Automatic GUI testing for android using reinforcement learning |
| title_fullStr |
Automatic GUI testing for android using reinforcement learning |
| title_full_unstemmed |
Automatic GUI testing for android using reinforcement learning |
| title_sort |
Automatic GUI testing for android using reinforcement learning |
| dc.creator.fl_str_mv |
Valbuena Bautista, Daniel |
| dc.contributor.advisor.none.fl_str_mv |
Mojica Hanke, Anamaría Irmgard Escobar Velásquez, Camilo Andrés Linares Vásquez, Mario |
| dc.contributor.author.none.fl_str_mv |
Valbuena Bautista, Daniel |
| dc.contributor.researchgroup.es_CO.fl_str_mv |
The Software Design Lab |
| dc.subject.keyword.none.fl_str_mv |
Reinforcement learning Testing Android |
| topic |
Reinforcement learning Testing Android Ingeniería |
| dc.subject.themes.es_CO.fl_str_mv |
Ingeniería |
| description |
The developers focus on testing applications, which can be a time-consuming task. To address this issue, we developed AgentDroid, a tool that utilizes reinforcement learning techniques to automate test execution. So far, the results have been impressive, outperforming state-of-the-art RL-based automated testing tools for Android, such as ARES. In fact, AgentDroid achieved a 20% improvement in cumulative coverage compared to ARES. However, its effectiveness has only been evaluated on a single application, making it challenging to find compatible apps for testing. To address this, we tested 61 open-source apps and successfully executed 11 to verify that the tool's performance was consistent. During this experimentation, we also identified and corrected bugs in the tool, improved error detection, and generated code coverage reports at the package, class, and method levels. |
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2023 |
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2023-01-30T18:22:28Z |
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2023-01-30T18:22:28Z |
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2023-01-28 |
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Trabajo de grado - Pregrado |
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eng |
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David Adamo, Md Khorrom Khan, Sreedevi Koppula, and Renée Bryce. "Reinforcement Learning for Android GUI Testing". In: Proceedings of the 9th ACM SIGSOFT International Workshop on Automating TEST Case Design, Selection, and Evaluation. New York, NY, USA: Association for Computing Machinery, 2018, 2-8 (cit. on pp. 5, 7-9, 11). Gigon Bae, Gregg Rothermel, and Doo-Hwan Bae. "Comparing model-based and dynamic event-extraction based GUI testing techniques: An empirical study". In: Journal of Systems and Software 97 (2014), pp. 15-46 (cit. on p. 5). Eliane Collins, Arilo Neto, Auri Vincenzi, and José Maldonado. "Deep Reinforcement Learning Based Android Application GUI Testing". In: Brazilian Symposium on Software Engineering. New York, NY, USA: Association for Computing Machinery, 2021, 186-194 (cit. on pp. 5, 8, 9). Edgar Camilo Díaz Suárez, Camila Pantoja Gómez, and Camilo Esteban Rozo Benitez. Automatic multi-platform Interaction testing for android using reinforcement learning. Tech. rep. Universidad de los Andes, 2022 (cit. on p. 18). Juha Eskonen, Julen Kahles, and Joel Reijonen. "Automating GUI Testing with Image-Based Deep Reinforcement Learning". In: 2020 IEEE International Conference on Autonomic Computing and Self-Organizing Systems (ACSOS). 2020, pp. 160-167 (cit. on pp. 8, 9, 11). Jakob N Foerster. "Deep multi-agent reinforcement learning". PhD thesis. University of Oxford, 2018 (cit. on p. 8). Tianxiao Gu, Chun Cao, Tianchi Liu, et al. "AimDroid: Activity-Insulated Multilevel Automated Testing for Android Applications". In: 2017 IEEE International Conference on Software Maintenance and Evolution (ICSME). 2017, pp. 103- 114 (cit. on pp. 5, 7-9). Ciaran Gultnieks. F-Droid - free and Open source android app repository. 2010 (cit. on p. 2). Yavuz Köroglu and Alper Sen. "Reinforcement Learning-Driven Test Generation for Android GUI Applications using Formal Specifications". In: CoRR abs/1911.05403 (2019). arXiv: 1911.05403 (cit. on pp. 7, 9). Mario Linares-Vásquez, Cárlos Bernal-Cardenas, Kevin Moran, and Denys Poshyvanyk. "How do Developers Test Android Applications?" In: 2017 IEEE International Conference on Software Maintenance and Evolution (ICSME). Sept. 2017, pp. 613-622 (cit. on p. 1). Ryan Lowe, Yi Wu, Aviv Tamar, et al. Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments. 2017 (cit. on p. 8). Nicolai A. Lynnerup, Laura Nolling, Rasmus Hasle, and John Hallam. A Survey on Reproducibility by Evaluating Deep Reinforcement Learning Algorithms on Real-World Robots. 2019. arXiv: 1909.03772 [cs.LG] (cit. on p. 26). Ke Mao, Mark Harman, and Yue Jia. "Sapienz: Multi-Objective Automated Testing for Android Applications". In: Proceedings of the 25th International Symposium on Software Testing and Analysis. ISSTA 2016. Saarbrücken, Germany: Association for Computing Machinery, 2016, 94-105 (cit. on p. 5). Minxue Pan, An Huang, Guoxin Wang, Tian Zhang, and Xuandong Li. "Reinforcement Learning Based Curiosity-Driven Testing of Android Applications". In: Proceedings of the 29th ACM SIGSOFT International Symposium on Software Testing and Analysis. ISSTA 2020. Virtual Event, USA: Association for Computing Machinery, 2020, 153-164 (cit. on pp. 7-9). Andrea Romdhana, Alessio Merlo, Mariano Ceccato, and Paolo Tonella. "Deep Reinforcement Learning for Black-Box Testing of Android Apps". In: CoRR abs/2101.02636 (2021). arXiv: 2101.02636 (cit. on pp. 1, 8, 9). Richard S. Sutton and Andrew G. Barto. Reinforcement Learning: An Introduction. Second. The MIT Press, 2018 (cit. on pp. 5-7). The Software Design Lab. InstruAPK. https://github.com/TheSoftwareDesignLab/ InstruAPK. 2020 (cit. on p. 15). The Software Design Lab. MutAPK. https://thesoftwaredesignlab.github.io/MutAPK (cit. on p. 15). Daniel Toyama, Philippe Hamel, Anita Gergely, et al. "AndroidEnv: A Reinforcement Learning Platform for Android". In: CoRR abs/2105.13231 (2021). arXiv: 2105.13231 (cit. on pp. 8, 11). Thi Anh Tuyet Vuong and Shingo Takada. "A Reinforcement Learning Based Approach to Automated Testing of Android Applications". In: Proceedings of the 9th ACM SIGSOFT International Workshop on Automating TEST Case Design, Selection, and Evaluation. New York, NY, USA: Association for Computing Machinery, 2018, 31-37 (cit. on pp. 7-9). Tuyet Vuong and Shingo Takada. "Semantic analysis for deep Q-network in android GUI testing". English. In: Proceedings - SEKE 2019. Proceedings of the International Conference on Software Engineering and Knowledge Engineering, SEKE. 31st International Conference on Software Engineering and Knowledge Engineering, SEKE 2019 ; Conference date: 10-07-2019 Through 12-07-2019. Knowledge Systems Institute Graduate School, Jan. 2019, pp. 123-128 (cit. on pp. 8, 9). Husam N. Yasin, Siti Hafizah Ab Hamid, and Raja Jamilah Raja Yusof. "DroidbotX: Test Case Generation Tool for Android Applications Using Q-Learning". In: Symmetry 13.2 (2021) (cit. on pp. 7-9). Yavuz Koroglu, Alper Sen, Ozlem Muslu, et al. "QBE: QLearning-Based Exploration of Android Applications". In: 2018 IEEE 11th International Conference on Software Testing, Verification and Validation (ICST). 2018, pp. 105-115 (cit. on pp. 7-9). Zhihao Shen, Kang Yang, Zhao Xi, Jianhua Zou, and Wan Du. "DeepAPP: A Deep Reinforcement Learning Framework for Mobile Application Usage Prediction". In: IEEE Transactions on Mobile Computing (2021), pp. 1-1 (cit. on p. 8). |
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Atribución 4.0 Internacionalhttp://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Mojica Hanke, Anamaría Irmgard8b69098e-58c5-4e7b-a9a8-f133293320ac600Escobar Velásquez, Camilo Andrés6c2a87eb-6631-4882-85f6-a9b038936472600Linares Vásquez, Mariovirtual::10719-1Valbuena Bautista, Daniele46bad11-207d-4954-b7cf-8617e290cbff600The Software Design Lab2023-01-30T18:22:28Z2023-01-30T18:22:28Z2023-01-28http://hdl.handle.net/1992/64317instname:Universidad de los Andesreponame:Repositorio Institucional Sénecarepourl:https://repositorio.uniandes.edu.co/The developers focus on testing applications, which can be a time-consuming task. To address this issue, we developed AgentDroid, a tool that utilizes reinforcement learning techniques to automate test execution. So far, the results have been impressive, outperforming state-of-the-art RL-based automated testing tools for Android, such as ARES. In fact, AgentDroid achieved a 20% improvement in cumulative coverage compared to ARES. However, its effectiveness has only been evaluated on a single application, making it challenging to find compatible apps for testing. To address this, we tested 61 open-source apps and successfully executed 11 to verify that the tool's performance was consistent. During this experimentation, we also identified and corrected bugs in the tool, improved error detection, and generated code coverage reports at the package, class, and method levels.Ingeniero de Sistemas y ComputaciónPregrado43 páginasapplication/pdfengUniversidad de los AndesIngeniería de Sistemas y ComputaciónFacultad de IngenieríaDepartamento de Ingeniería Sistemas y ComputaciónAutomatic GUI testing for android using reinforcement learningTrabajo de grado - Pregradoinfo:eu-repo/semantics/bachelorThesisinfo:eu-repo/semantics/acceptedVersionhttp://purl.org/coar/resource_type/c_7a1fTexthttp://purl.org/redcol/resource_type/TPReinforcement learningTestingAndroidIngenieríaDavid Adamo, Md Khorrom Khan, Sreedevi Koppula, and Renée Bryce. "Reinforcement Learning for Android GUI Testing". In: Proceedings of the 9th ACM SIGSOFT International Workshop on Automating TEST Case Design, Selection, and Evaluation. New York, NY, USA: Association for Computing Machinery, 2018, 2-8 (cit. on pp. 5, 7-9, 11).Gigon Bae, Gregg Rothermel, and Doo-Hwan Bae. "Comparing model-based and dynamic event-extraction based GUI testing techniques: An empirical study". In: Journal of Systems and Software 97 (2014), pp. 15-46 (cit. on p. 5).Eliane Collins, Arilo Neto, Auri Vincenzi, and José Maldonado. "Deep Reinforcement Learning Based Android Application GUI Testing". In: Brazilian Symposium on Software Engineering. New York, NY, USA: Association for Computing Machinery, 2021, 186-194 (cit. on pp. 5, 8, 9).Edgar Camilo Díaz Suárez, Camila Pantoja Gómez, and Camilo Esteban Rozo Benitez. Automatic multi-platform Interaction testing for android using reinforcement learning. Tech. rep. Universidad de los Andes, 2022 (cit. on p. 18).Juha Eskonen, Julen Kahles, and Joel Reijonen. "Automating GUI Testing with Image-Based Deep Reinforcement Learning". In: 2020 IEEE International Conference on Autonomic Computing and Self-Organizing Systems (ACSOS). 2020, pp. 160-167 (cit. on pp. 8, 9, 11).Jakob N Foerster. "Deep multi-agent reinforcement learning". PhD thesis. University of Oxford, 2018 (cit. on p. 8).Tianxiao Gu, Chun Cao, Tianchi Liu, et al. "AimDroid: Activity-Insulated Multilevel Automated Testing for Android Applications". In: 2017 IEEE International Conference on Software Maintenance and Evolution (ICSME). 2017, pp. 103- 114 (cit. on pp. 5, 7-9).Ciaran Gultnieks. F-Droid - free and Open source android app repository. 2010 (cit. on p. 2).Yavuz Köroglu and Alper Sen. "Reinforcement Learning-Driven Test Generation for Android GUI Applications using Formal Specifications". In: CoRR abs/1911.05403 (2019). arXiv: 1911.05403 (cit. on pp. 7, 9).Mario Linares-Vásquez, Cárlos Bernal-Cardenas, Kevin Moran, and Denys Poshyvanyk. "How do Developers Test Android Applications?" In: 2017 IEEE International Conference on Software Maintenance and Evolution (ICSME). Sept. 2017, pp. 613-622 (cit. on p. 1).Ryan Lowe, Yi Wu, Aviv Tamar, et al. Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments. 2017 (cit. on p. 8).Nicolai A. Lynnerup, Laura Nolling, Rasmus Hasle, and John Hallam. A Survey on Reproducibility by Evaluating Deep Reinforcement Learning Algorithms on Real-World Robots. 2019. arXiv: 1909.03772 [cs.LG] (cit. on p. 26).Ke Mao, Mark Harman, and Yue Jia. "Sapienz: Multi-Objective Automated Testing for Android Applications". In: Proceedings of the 25th International Symposium on Software Testing and Analysis. ISSTA 2016. Saarbrücken, Germany: Association for Computing Machinery, 2016, 94-105 (cit. on p. 5).Minxue Pan, An Huang, Guoxin Wang, Tian Zhang, and Xuandong Li. "Reinforcement Learning Based Curiosity-Driven Testing of Android Applications". In: Proceedings of the 29th ACM SIGSOFT International Symposium on Software Testing and Analysis. ISSTA 2020. Virtual Event, USA: Association for Computing Machinery, 2020, 153-164 (cit. on pp. 7-9).Andrea Romdhana, Alessio Merlo, Mariano Ceccato, and Paolo Tonella. "Deep Reinforcement Learning for Black-Box Testing of Android Apps". In: CoRR abs/2101.02636 (2021). arXiv: 2101.02636 (cit. on pp. 1, 8, 9).Richard S. Sutton and Andrew G. Barto. Reinforcement Learning: An Introduction. Second. The MIT Press, 2018 (cit. on pp. 5-7).The Software Design Lab. InstruAPK. https://github.com/TheSoftwareDesignLab/ InstruAPK. 2020 (cit. on p. 15).The Software Design Lab. MutAPK. https://thesoftwaredesignlab.github.io/MutAPK (cit. on p. 15).Daniel Toyama, Philippe Hamel, Anita Gergely, et al. "AndroidEnv: A Reinforcement Learning Platform for Android". In: CoRR abs/2105.13231 (2021). arXiv: 2105.13231 (cit. on pp. 8, 11).Thi Anh Tuyet Vuong and Shingo Takada. "A Reinforcement Learning Based Approach to Automated Testing of Android Applications". In: Proceedings of the 9th ACM SIGSOFT International Workshop on Automating TEST Case Design, Selection, and Evaluation. New York, NY, USA: Association for Computing Machinery, 2018, 31-37 (cit. on pp. 7-9).Tuyet Vuong and Shingo Takada. "Semantic analysis for deep Q-network in android GUI testing". English. In: Proceedings - SEKE 2019. Proceedings of the International Conference on Software Engineering and Knowledge Engineering, SEKE. 31st International Conference on Software Engineering and Knowledge Engineering, SEKE 2019 ; Conference date: 10-07-2019 Through 12-07-2019. Knowledge Systems Institute Graduate School, Jan. 2019, pp. 123-128 (cit. on pp. 8, 9).Husam N. Yasin, Siti Hafizah Ab Hamid, and Raja Jamilah Raja Yusof. "DroidbotX: Test Case Generation Tool for Android Applications Using Q-Learning". In: Symmetry 13.2 (2021) (cit. on pp. 7-9).Yavuz Koroglu, Alper Sen, Ozlem Muslu, et al. "QBE: QLearning-Based Exploration of Android Applications". In: 2018 IEEE 11th International Conference on Software Testing, Verification and Validation (ICST). 2018, pp. 105-115 (cit. on pp. 7-9).Zhihao Shen, Kang Yang, Zhao Xi, Jianhua Zou, and Wan Du. "DeepAPP: A Deep Reinforcement Learning Framework for Mobile Application Usage Prediction". 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