JISE


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Journal of Information Science and Engineering, Vol. 36 No. 3, pp. 513-533


Detecting Duplicate Actions in a KDT Script Based on LRS and LCS


CHIEN-HUNG LIU, WOEI-KAE CHEN AND CHEN-YAN LIAO
Department of Computer Science and Information Engineering
National Taipei University of Technology
Taipei, 106 Taiwan
E-mail: {cliu; wkchen; t104598007}@ntut.edu.tw


In keyword-driven testing (KDT), having duplicate actions in the test script is perhaps the most common bad smell. Once the target user interface is changed, a KDT script with duplicate actions can be difficult to maintain. Thus, detecting and removing duplicate actions is an important task. However, so far, no KDT testing tools support automated duplicate detection. This paper proposes a method and tool, called DDT (Duplicate script Detection Tool), for the tester to quickly identify duplicate actions. Two detection algorithms based on Longest Repeated Substring (LRS) and Longest Common Subsequence (LCS) are presented. In addition, DDT provides a keyword extraction feature that can automatically remove duplicate actions. Our experimental results show that there are 21-42% of duplicate actions in a typical KDT script, DDT can detect these duplicate actions in 3-5 seconds, and up to 58-81% of these duplicate actions should be refactored.


Keywords: duplicate detection, keyword extraction, bad smell, KDT scripts, duplicate actions

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