## 3 Approach and Methodology
First is the **tracing phase**, in which AbstractTrace takes the test suite designed by the test engineer and builds a dataset of execution traces, wherein each trace (string) encapsulates the execution behavior of a passed test case. The second phase is the **analysis phase**, in which the dataset of strings is analyzed using various string-similarity algorithms to identify the similarity score between every pair of test cases. AbstractTrace evaluates this dataset to:  
1. Identify redundant test cases;
2. Cluster test cases based on their string similarity scores; and
3. Rank test cases from most to least similar. Finally, in the **optimization phase**, the test engineer has more insight into the test suite. In this phase, engineers can optimize the test suite by removing similar test cases; those that generate the exact execution trace. Test cases with similar execution behaviors will exhibit comparable string representations. This analysis enables test engineers to eliminate redundant test cases and select a subset that aligns with their testing objectives and goals. Additionally, this execution-trace dataset is updated and maintained during the maintenance of the test suite. Figure 2 shows that AbstractTrace employs a hierarchical execution-trace system comprising three levels. The top level utilizes four distinct classes, the mid-level incorporates 16 classes, and the lowest level captures events at the source-code level. Algorithm 1 shows the proposed procedure that can be used to optimize the test suite and improve its code coverage. The approach takes the test suite \(T_{j}\) and the current version of the program under test \(P_{j}\) as inputs. Then, it will run the program with the given test suite, collect execution traces for each of the passed test cases, analyze the traces, and build a similarity matrix \(SM_{jk}\) for every dataset of traces. This \(SM_{jk}\) will be used to find the redundant test cases. Redundant test cases are those with the similarity \(score=1\). Moreover, a similarity matrix can be built to rank and cluster these test cases. A high similarity score between any two  
Figure 1: AbstractTrace’s approach starts by evaluating a test suite on a program under test, and builds a dataset of execution traces collected for all passed test cases. In the analysis part, all the execution traces are evaluated to find similarities using various algorithms. This similarity score can be used to select and estimate future regression testing whenever it is needed. \(TC_{i}\) stands for test-case number \(i\), and \(ETR_{i}\) stands for the execution-trace number \(i\) that is associated with test case \(i\) or \(TC_{i}\). test cases in the matrix indicates a high similarity between the test cases' execution behavior. This similarity score can be based on a clustering mechanism that will help the test engineer select the test cases in future testing rounds, including the regression testing process. It will help better manage the testing process. ```
\(L_{x}\): A high-level abstract view encapsulates all runtime activities into a set of 4 event classes, which summarize resources and memory allocations, operations on various data structures, string processing, and general expression evaluations. \(L_{x}\): A mid-level abstract view of various runtime activities. A total of 16 event classes summarizes various data structure allocations, usage and operations, string processing, and various expression evaluations and processing. \(L_{x}\): A detailed source code level runtime events. A total of 68 kinds of event plus line number change (\(L_{x}\), \(L_{x}\)) and syntactic structure change (\(L_{x}\), \(L_{x}\)). ```  
**Algorithm 1** AbstractTrace Procedure  
**Remark 1**. : _The program under test, where \(j\) is the current version of the program_  
**Example 2**.