Computer Science

Computer Science ›› 2020, Vol. 47 ›› Issue (6): 44-50.doi: 10.11896/jsjkx.191100133

• Intelligent Software Engineering • Previous Articles     Next Articles

Defect Recognition of APP Software Based on User Feedback

DUAN Wen-jing, JIANG Ying   

  1. Computer Technology Application Key Lab of Yunnan Province,Kunming 650500,China
    Faculty of Information Engineering and Automation,Kunming University of Science and Technology,Kunming 650500,China
  • Received:2019-11-18 Online:2020-06-15 Published:2020-06-10
  • About author:DUAN Wen-jing,born in 1992,postgraduate,is a member of China Compu-ter Federation.Her main research inte-rests include software engineering and so on.
    JIANG Ying,born in 1974,Ph.D,professor,Ph.D supervisor,is a senior member of China Computer Federation.Her main research interests include software quality assurance and testing,cloud computing,big data analysis and intelligent software engineering.
  • Supported by:
    This work was supported by the National Key Research and Development Program of China (2018YFB1003904),National Natural Science Foundation of China (61462049,61063006,60703116) and Key Project of Yunnan Applied Basic Research (2017FA033)

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Abstract: At present,APP software has been widely used,and its quality has been widely concerned.The high quality software defects should be fewer.However,software testing cannot find all defects.Some software defects can not be found until the user uses the software.This paper puts forward a method of software defect recognition based on user feedback.By defining the APP software defect extraction rule,software defects in user feedback are mined.During the mining of APP software’s defect,the extraction rule is dynamically updated.Then the classification and severity for the extracted defects are analyzed.The experimental results show that the proposed method is effective,the accuracy of extracting user comments including APP software’s defects is 85.19%,and the accuracy of defect classification is 83.23%.

Key words: APP software, Defect classification, Defect extraction rules, Defect severity, User feedback

CLC Number: 

  • TP311.5
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