## 9 Conclusions  
Software testing often involves many test cases (_test suites_) that require effort and time to manage and validate. A test suite can easily include redundant and overlapping test cases that can be reduced and eliminated without affecting the overall software quality. Optimizing a _bloated test suite_ by identifying, removing, and reducing redundant or non-relevant test cases without compromising the software quality is challenging. Thus, finding these redundant and highly similar test cases is not an easy task considering (1) the number of test cases included in a test site, (2) the dynamic behavior of the execution program, and (3) the high associations between the input value (test case input) and its runtime behaviors concerning code coverage [79; 80].  
It is clear that analyzing and clustering these traces can result in clustering their test cases. In this study, we introduced the concept of AbstractTrace and its associated execution trace. The presented approach aims at enhancing software testing processes. Our findings reveal several key insights: (1) **Similarity Measures**: AbstractTrace enables test engineers to quantify the similarity between test cases based on code structure and behavior, providing a new view of the test suite. (2) **Ranking and Grouping**: Test engineers can rank test cases based on similarity scores. This ranking informs decisions about test-case execution priority. High-similarity test cases can be grouped, while less similar ones receive individual attention. (3) **Code-Coverage Alignment**: AbstractTrace bridges the gap between similarity and code coverage. It ensures that test cases are executed based on their impact on code structure. No more blind selection; each test case contributes meaningfully to coverage. (4) **Priority-Based Execution**: Our proposed prioritization technique encourages a strategic approach. High-priority test cases take center stage, while common or overlapping cases wait their turn. Finally, (5) **Test-Case Relationships**: AbstractTrace illuminates relationships within the suite beyond individual test cases. It unveils hidden connections, allowing test engineers to identify patterns and dependencies. In summary, AbstractTrace equips test engineers with powerful tools for optimizing testing efforts. By emphasizing differences, aligning with code coverage, and prioritizing strategically, we pave the way for more efficient and effective software validation.  
However, this research paper is limited to proving the concept with simple case studies. Future research will be focused on evaluating this approach's practical implementation in software development and testing. Additionally, we plan to evaluate different machine learning models and assess their use during the software testing process.  
Conceptualization, Z.A.A.-S.; Methodology, Z.A.A.-S.; Validation, Z.A.A.-S.; Investigation, Z.A.A.-S.; Resources, C.L.J.; Supervision, C.L.J. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Jordan University of Science and Technology (JUST), Irbid 22110, Jordan, as part of the corresponding author's sabbatical leave.  
Not applicable.