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Web Application Testing Using Deep Reinforcement Learning | ||
| مجله مهندسی برق دانشگاه تبریز | ||
| مقاله 9، دوره 54، شماره 2 - شماره پیاپی 108، مرداد 1403، صفحه 219-227 اصل مقاله (457.88 K) | ||
| نوع مقاله: علمی-پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22034/tjee.2023.17047 | ||
| نویسندگان | ||
| محمدرضا عباس نژاد1؛ امیر جهانگرد رفسنجانی* 2؛ امین میلانی فرد3 | ||
| 1دانشکده مهندسی کامپیوتر، دانشگاه یزد، یزد، ایران. | ||
| 2عضو هیات علمی گروه کامپیوتر/دانشگاه یزد | ||
| 3دانشگاه صنعتی نیویورک پردیس ونکوور، ونکوور، بریتیش کلمبیا، کانادا. | ||
| چکیده | ||
| Web applications (apps) are integral to our daily lives. Before users can use web apps, testing must be conducted to ensure their reliability. There are various approaches for testing web apps. However, they still require improvement. In fact, they struggle to achieve high coverage of web app functionalities. On the one hand, web apps typically have an extensive state space, which makes testing all states inefficient and time-consuming. On the other hand, specific sequences of actions are required to access certain functionalities. Therefore, the optimal testing strategy extremely depends on the app’s features. Reinforcement Learning (RL) is a machine learning technique that learns the optimal strategy to solve a task through trial-and-error rather than explicit supervision, guided by positive or negative reward. Deep RL extends RL, and exploits the learning capabilities of neural networks. These features make Deep RL suitable for testing complex state spaces, such as those found in web apps. However, modern approaches support fundamental RL. We have proposed WeDeep, a Deep RL testing approach for web apps. We evaluated our method using seven open-source web apps. Results from experiments prove it has higher code coverage and fault detection than other existing methods | ||
| کلیدواژه‌ها | ||
| Deep reinforcement learning؛ Automated testing؛ Test generation؛ Web application | ||
| مراجع | ||
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