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
| Summary: | 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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