Automation testing is widely used in the functional testing of web applications. However, during the evolution of web applications, such web test scripts tend to break. It is essential to repair such broken test scripts to make regression testing run successfully. As manual repairing is time-consuming and expensive, researchers focus on automatic repairing techniques. Empirical study shows that the web element locator is the leading cause of web test breakages. Most existing repair techniques utilize Document Object Model attributes or visual appearances of elements to find their location but neglect their semantic information.

This paper proposes a novel semantic repair technique called \textbf{Sem}antic \textbf{Te}st \textbf{R}epair (Semter) for web test repair. Our approach captures relevant semantic information from test executions on the application’s basic version and locates target elements by calculating semantic similarity between elements to repair tests. Our approach can also repair test workflow due to web page additions or deletions by a local exploration in the updated version. We evaluated the efficacy of our technique on six real-world web applications compared with three baselines. Experimental results show that Semter achieves an 84% average repair ratio within an acceptable time cost, significantly outperforming the state-of-the-art web test repair techniques.