融合文本与分类信息的重复缺陷报告检测方法

doi:10.11896/jsjkx.181102232

Abstract

Abstract: Software defect is the root of software errors and failures.Software defect is caused by unreasonable requirement analysis,imprecise programming language and lack of experience of developers.Software defects are inevitable,and submitting defect reports is an important way to find and improve defects.Defect report is the carrier of describing defects,and the repair of defect report is the necessary means to improve software.Maintenance personnel and users submit reports for the same defect repeatedly,resulting in a large number of redundant reports in the defect report library.Manual triage is unable to adapt to more and more complex software systems.The detection of duplicate defect reports can filter redundant duplicate reports from defect report libraries and invests human and time in new defect reports.The prediction accuracy rate of current research methods is not high,and the difficulty is to find a suitable and comprehensive method to measure the similarity between defect reports.Based on the idea of the integration method and the python language,a new method named BSO (combination of BM25F,LSI and One-Hot) for detecting duplicate defect report was proposed by using text information and categorization information.On the basis of data preprocessing,duplicate defect report is divided into text information domain and categorization information domain.BM25F and LSI algorithms are used to get similarity scores in text information domain,and One-Hot algorithm is used to get similarity scores in categorization information domain.The similarity fusion method is used to synthesize the similarity score between text information domain and categorization information domain,and a recommendation list for each defect report corresponds to a duplicate defect report.The accuracy of the duplicate defect report detection is calculated.Compared with the baseline method and the state-of the art methods including REP and DBTM on OpenOffice.The experimental results show that the accuracy of the proposed method is 4.7% higher than that of DBTM,6.3% higher than that of REP,and higher than that of baseline method.Experiment results fully prove the effectiveness of BSO method.

Key words: Duplicate defect report, Information retrieval method, One-Hot, Similarity fusion, Topic model

CLC Number:

TP311.5

FAN Dao-yuan, SUN Ji-hong, WANG Wei, TU Ji-ping, HE Xin. Detection Method of Duplicate Defect Reports Fusing Text and Categorization Information[J].Computer Science, 2019, 46(12): 192-200.

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