Abstract <p>Automatic testing technology can reduce production costs and improve output rates, gradually replacing time-consuming and laborious manual testing. It is gradually replacing time-consuming and labor-intensive manual testing. However, there are currently significant development difficulties and high maintenance costs in automated testing systems. The testing work is too focused on software maintenance and case correction, resulting in a significant gap between the actual application results and expectations. In view of this, a lighter automated testing system is built based on the Vue.js framework. Considering the strong data dependency in existing detection algorithms, a reinforcement learning testing algorithm based on Sarsa is proposed to enhance the flexibility of testing. The results showed that the automated testing model had higher testing coverage, testing efficiency, and fault detection volume on the software program dataset F-Droid, with 87.5%, 90.1%, and 1003 respectively, all higher than the comparison algorithm. In robot motion control testing, the model had a lower root mean square error of 1.24%. The comparative model couldn’t converge or converge to over 5.0%. This indicates that the automated testing system improves testing efficiency and accuracy, help to reduce testing costs, and ensure system stability and operational quality.</p>