# Attention Transfer Reinforcement Learning for Test Case Prioritization in Continuous Integration
## 5 Experimental Design
### Public Datasets  
To verify the usability of sorting methods in practical scenarios, experiments were conducted on four industrial datasets (Paint Control, IOF/ROL, GSDTSR, and Apach Drill). Among them, Paint Control and IOF/ROL are from ABB Robotics North, which are test datasets for complex industrial robots. GSDTSR is Google's product test dataset.  
Figure 4: Architecture of Actor and Critic.  
Apach Drill is an industrial dataset obtained using travisorent. These datasets include the historical execution results of test cases, estimated execution time, last execution time, and current integration cycle execution results. In [9], the Paint Control, IOF/ROL, and GSDTSR datasets were obtained. Apach Drill was retrieved from an open-source database [30]. The specific statistical data are shown in Table 2.  
In Table 2, \"Test Cases\" denotes the total number of distinct test cases encountered across all integration cycles. \"CI Cycles\" indicates the number of integration and testing cycles the code has undergone. \"Verdicts\" represents the total number of executions of all test cases across all integration cycles. \"Failed\" shows the proportion of failed tests among all test executions. \"Frequency\" indicates the recurrence rate of test cases. A higher \"Failed\" value signifies a bigzher fault-detection capability of the test cases, while a higher \"Frequency\" value indicates more extensive historical information for the test cases.  
Test cases in Paint Control are executed multiple times and provide rich historical information. This characteristic is particularly beneficial for models like RETECS, which rely on test case features as input. However, for methods like PRM, which focus on the relative relationships between test cases, excessive emphasis on relative order leads to insufficient utilization of individual test case features, causing poor performance on such low-data-volume datasets. In contrast, the GSDTSR dataset, which is larger in scale with a greater number of test cases, lacks sufficient historical execution information for individual test cases. This limitation makes it difficult for methods like RETECS to obtain adequate prioritization references, leading to suboptimal prioritization performance.  
Therefore, RETECS performs well in tasks where individual test cases have rich historical feature information, while PRM excels in scenarios where individual test case features are insufficient and require a global perspective for prioritization. This study selects these two representative methods for comparison in their respective advantage domains to demonstrate that our approach can achieve robust performance in complex scenarios.  
From these four datasets, Paint Control represents data with rich historical information and high fault-detection capability; IOF/ROL represents data with limited historical information but high fault-detection capability; GSDTSR represents data with rich historical information but low fault-detection capability; and Apach Drill represents data with limited historical information and low fault-detection capability. These datasets adequately represent regression test data in CI processes, ensuring the comprehensiveness of experimental validation.