TY - JOUR
T1 - Research on Defect Priority Classification of Crowdsourcing Testing for Mobile Applications
AU - Liu, Ying
AU - Ma, Chunyan
AU - Dong, Zhanwei
AU - Zhang, Tao
AU - Cheng, Jing
AU - Zhang, Jie
N1 - Publisher Copyright:
© 2020 Published under licence by IOP Publishing Ltd.
PY - 2020/5/20
Y1 - 2020/5/20
N2 - Crowdsourcing testing technology has developed in recent years with the development of software testing, which can speed up releasing cycle and improve the quality of testing. It is of great practical value to study the priority classification and cause analysis of defect reports by using the potential information of crowdsourcing test defect reports. This paper combines the research of mobile application crowdsourcing test defect report with machine learning data analysis technology, studies the priority classification of mobile application crowdsourcing test defect report, and then carries out defect cause analysis on the basis of defect priority classification. Defect classification is an intuitive reflection of defect research. This paper takes defect priority classification as the breakthrough point of defect report research, uses σ-AdaBoostSVM classification algorithm to classify defect reports, and then carries out cause analysis after defect report classification, which is conducive to the faster location and repair of defects. The experimental verification results demonstrate the effectiveness of the proposed method.
AB - Crowdsourcing testing technology has developed in recent years with the development of software testing, which can speed up releasing cycle and improve the quality of testing. It is of great practical value to study the priority classification and cause analysis of defect reports by using the potential information of crowdsourcing test defect reports. This paper combines the research of mobile application crowdsourcing test defect report with machine learning data analysis technology, studies the priority classification of mobile application crowdsourcing test defect report, and then carries out defect cause analysis on the basis of defect priority classification. Defect classification is an intuitive reflection of defect research. This paper takes defect priority classification as the breakthrough point of defect report research, uses σ-AdaBoostSVM classification algorithm to classify defect reports, and then carries out cause analysis after defect report classification, which is conducive to the faster location and repair of defects. The experimental verification results demonstrate the effectiveness of the proposed method.
UR - http://www.scopus.com/inward/record.url?scp=85085518664&partnerID=8YFLogxK
U2 - 10.1088/1742-6596/1518/1/012008
DO - 10.1088/1742-6596/1518/1/012008
M3 - 会议文章
AN - SCOPUS:85085518664
SN - 1742-6588
VL - 1518
JO - Journal of Physics: Conference Series
JF - Journal of Physics: Conference Series
IS - 1
M1 - 012008
T2 - 2020 4th International Conference on Machine Vision and Information Technology, CMVIT 2020
Y2 - 20 February 2020 through 22 February 2020
ER -