Int J Performability Eng ›› 2020, Vol. 16 ›› Issue (8): 1279-1288.doi: 10.23940/ijpe.20.08.p15.12791288

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Incremental Data Mining-based Software Failure Detection

Pan Liua,b and Wulan Huanga,*   

  1. aFaculty of Business Information, Shanghai Business School, Shanghai, 201400, China;
    bEngineering Research Center for Software Testing and Evaluation of Fujian Province, Xiamen, 361000, China
  • Submitted on ; Revised on ; Accepted on
  • Contact: *E-mail address:
  • About author:Pan Liu received the MSc degree in computer software and theory from Nanchang University in 2006 and the PhD degree in computer application from Shanghai University in 2011. Now he is a professor at Faculty of Business Information, Shanghai Business School, Shanghai, China. He is also a researcher in Shanghai Key Laboratory of Computer Software Testing & Evaluating, Shanghai, China. His papers have been published in some well-known international journals, and ACM and IEEE conferences. His main interests include software testing, model-based testing, formal method, and algorithm design.Wulan Huang received the MSc degree in Circuit and system from Hunan Normal University in 2005 and the PhD degree in Management Science and Engineering from Shanghai University of Finance and Economics in 2017. Now she is a lecturer at Faculty of Business Information, Shanghai Business School, Shanghai, China. She is a principal investigator of a project supported by National Social Science Fund of China. Her main interests include algorithm design, data analysis and knowledge management.

Abstract: It has been proved by practice that mining weblogs to detect software errors is an effective software testing method. This paper presents a software failure detection method based on the incremental mining weblog strategy and gives data mining steps for the implementation of this method. A case is studied for the proposed test method. In this case, we use Splunk, a data analysis tool, to analyze some weblogs that record some linked information of mobile applications for android. The result of the data analysis shows that the proposed method can effectively detect the software failure problem in the download process of mobile applications. Therefore, the proposed method can be used for software reliability assessment.

Key words: incremental data mining, software failure detection, weblog, Splunk, reliability assessment