## 3 Approach and Methodology  
This paper employs program execution traces to capture execution behavior and code coverage of test cases. Under the execution trace in the context of this research article, we use source-code-level runtime events, such as function calls, expression evaluations, dynamic memory allocations, etc., to represent the execution behavior of a test case. The set of 70 different runtime events, presented in Table 1, are traced during the execution of the test case. The result of the execution trace of different test cases is used to create a dataset instance that represents the test suite. The execution traces from various test cases are collected and analyzed to detect the similarities between different test cases. This approach (1) provides a systematic mechanism to study and analyze test suites, (2) simplifies the process of identifying redundant test cases that can be eliminated without impacting the quality of the testing process, and (3) ranks these test cases within a test suite based on their similarity score. The goal is to formalize the process of removing redundant test cases, which should improve the testing process and reduce the overall testing time [22; 23]. Therefore, this paper proposes an approach that enables test engineers to score the similarities between various test cases within a test suite. Experimentation shows that this approach helps identify highly similar test cases and rank these test cases based on their similarity scores [24]. The two case studies provided show that the ranking system allows the engineer to gain insight into the code coverage of the test suite. The approach offers a systematic labeling of test cases in a test suite with a score that indicates the test case and its code coverage score. It also allows the engineer to compare different scores from different test cases in the test suite. This will enable engineers to identify test cases that cover the same set of statements (_share the same code coverage_), which helps to identify the least-impacting test cases in terms of code coverage (_redundant and highly similar test cases_). Therefore, this formal mechanism enables an approach to remove test cases that do not contribute to code coverage within the test suite. This mechanism leads to a reduced number of test cases, reduced effort and time needed to validate these test suites, and an improved overall testing process. Moreover, the approach equips engineers with the ability to prioritize test cases, better select a representative subset of the test cases to run, and eventually estimate the testing time and cost according to the selected quality level. To achieve these capabilities, the approach uses the execution traces obtained from the test cases; each test case is associated with its string representation of the execution trace: a sequence of letters, each of which represents a unique runtime event. The combined sequence can be unique to represent one of the test cases. A dataset of these _trace strings_ is maintained for further analysis using various techniques, including different string-similarity algorithms and modern AI approaches. Trace strings are evaluated to find the similarities between test cases, classify these test cases based on their similarity score, and rank test cases based on their similarity measures. The test engineer can select the subset of test cases to be considered in the next phase of the testing process, which is often included during regression testing. Figure 1 presents an abstract visual representation of our approach and methodology. During the software development life cycle, the test engineer designs and executes a  
test suite to verify that the actual outputs and behaviors align with the expected results. Throughout this process, AbstractTrace can be employed. It encapsulates three different phases.