AbstractTrace: The Use of Execution Traces to Cluster, Classify, Prioritize, and Optimize a Bloated Test Suite.
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| Title: | AbstractTrace: The Use of Execution Traces to Cluster, Classify, Prioritize, and Optimize a Bloated Test Suite. |
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| Authors: | Al-Sharif, Ziad A., Jeffery, Clinton L. |
| Source: | Applied Sciences (2076-3417); Dec2024, Vol. 14 Issue 23, p11168, 29p |
| Subject Terms: | COMPUTER software quality control, SOFTWARE engineering, MACHINE learning, CLUSTER analysis (Statistics) |
| Abstract: | Due to the incremental and iterative nature of the software testing process, a test suite may become bloated with redundant, overlapping, and similar test cases. This paper aims to optimize a bloated test suite by employing an execution trace that encodes runtime events into a sequence of characters forming a string. A dataset of strings, each of which represents the code coverage and execution behavior of a test case, is analyzed to identify similarities between test cases. This facilitates the de-bloating process by providing a formal mechanism to identify, remove, and reduce extra test cases without compromising software quality. This form of analysis allows for the clustering and classification of test cases based on their code coverage and similarity score. This paper explores three levels of execution traces and evaluates different techniques to measure their similarities. Test cases with the same code coverage should generate the exact string representation of runtime events. Various string similarity metrics are assessed to find the similarity score, which is used to classify, detect, and rank test cases accordingly. Additionally, this paper demonstrates the validity of the approach with two case studies. The first shows how to classify the execution behavior of various test cases, which can provide insight into each test case's internal behavior. The second shows how to identify similar test cases based on their code coverage. [ABSTRACT FROM AUTHOR] |
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| Database: | Complementary Index |
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