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Improving Cluster Test Selection Techniques of Regression Testing by Slice Filtering

Author: DuanYongWei
Tutor: ZhaoZhiHong
School: Nanjing University
Course: Computer Science and Technology
Keywords: Regression test selection Clustering Selection Cluster analysis Program slicing
CLC: TP311.53
Type: Master's thesis
Year: 2011
Downloads: 13
Quote: 0
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Abstract


Regression testing is a test carried out on the modified program . Its purpose is to prevent modify a negative impact on the unmodified portion of the program . This is a very time-consuming work , because regression testing requirements to all existing test cases to re- execute again . Regression test selection technique to select a subset of the concentrate from the existing test cases as a new set of test cases for regression testing , in order to reduce the cost of regression testing . The cluster selection technology is a new regression test selection techniques . It is based on the similarity of each test case in software behavior to select . The execution profiles collected in the software development process of this technology , and the cluster analysis of these execution profile . Have similar execution profile test cases will be assigned to the same category in the cluster . Then sampling some typical test cases from each class cluster which is part of a new set of test cases . The sampling can choose many sampling strategy . Wherein the adaptive sampling strategy is an effective and widely used strategy . The program slice to improved clustering selection techniques . First , calculate the changes in procedures at a static program slicing slicing criterion . Secondly , through this slice those identified in the program is running may be modified impact to the program statement . Then, the cluster analysis of execution profiles of these statements . Finally, extract some test cases from clustering each cluster to form a smaller set of test cases . By reducing the dimension of the cluster analysis , slice filtering technology to greatly improve the ability of cluster selection techniques in large-scale software applications . Experimental results show that the filtering, clustering by slicing the efficiency of the implementation of the technology has been significantly improved , and clustering select the generated set of test cases has a high error detection capability .

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