# Test Suite Prioritization Based on Optimization Approach Using Reinforcement Learning
## 4 Results and Discussion
### Significance and Comparison  
This section discusses the significance, comparison, and threat to the validity of the proposed TSP technique. To compare the case study applications, the same number of faults were injected in all five applications to evaluate the prioritized order of test cases using the faults detection rate. The obtained results show that the average percent of fault detection of the random technique is 48.2 percent, the average percent of fault detects of the t-SANT technique is 82.8 percent, and the average percent of fault detection of the proposed technique is 87.6 percent based on all five applications' fault detect results. These results assert that the Q-Learning algorithm is a beneficial technique for TSP, and it detects maximum faults in minimum test cases to reduce the testing efforts. The comparison of the proposed approach with other TSP approaches is presented in Figure 5.  
The Threats to the Validity  
Our method of TSP based on RL is designed for the android-based case study application. It relies on converting sequence patterns extracted from the user and tester logs to a matrix. If the RL model's testing accuracy is low, the testing environment matrix obtained from the sequences needs improvement. In this case, the prioritized sequence may not capture the characteristics of the specified logs. Hence, it leads to the poor performance of TSP. In the experimentation, we adopt five datasets prepared from android applications to evaluate the performance of the proposed TSP method. The performance of the proposed method may be different for various datasets. However, it is more stable than other TSP techniques used for the comparison. We incorporated several open-source software codes to support the main proposal of this study. The advantages of open-source software are  
Figure 5: Comparison of the proposed TSP approach with baseline approaches.  
broad applications and continuous updates. In addition, evaluation results of the proposed TSP approach were thoroughly reviewed by experts in the field. Previous studies have broadly utilized the evaluation metrics involved in our experiments. For instance, the fault detection rate is a prominent metric for assessing the significance of TSP techniques.  
Moreover, we compared two widely used baseline methods in the literature for the TSP. Because we have not yet found any prioritization techniques specifically designed for testing android source code. Therefore, we compared the proposed methodology with random and t-SANT prioritization algorithms. The proposed RL-based TSP approach is a step toward a promising research direction to apply deep Q learning (DQN) and other reinforcement learning algorithm with convergence in existing TSP and TSM techniques. As future work, incorporating more complicated software programs can be used for the validation to prove the practicality of the proposed TSP technique. Moreover, the case study applications were chosen based on their availability and open-source code. Our experiments used a relatively small number of fault seedings, and various thresholds can be considered to prove the sustainability of the proposed approach. Although our experiment did not involve large-scale programs with millions of lines of code, we believe that our methodology can be generalized to other complex applications.