Mining Software Repository for Cleaning Bugs Using Data Mining Technique

作     者：Nasir Mahmood Yaser Hafeez Khalid Iqbal Shariq Hussain Muhammad Aqib Muhammad Jamal Oh-Young Song 

作者机构：University Institute of Information TechnologyPir Mehr Ali Shah Arid Agriculture UniversityRawalpindi46000Pakistan Department of Computer ScienceCOMSATS University IslamabadAttock CampusAttock43600Pakistan Department of Software EngineeringFoundation University IslamabadIslamabad44000Pakistan Department of Mathematics and StatisticsPir Mehr Ali Shah Arid Agriculture UniversityRawalpindi46000Pakistan Department of SoftwareSejong UniversitySeoul05006Korea 

出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)

年 卷 期：2021年第69卷第10期

页      面：873-893页

核心收录：

学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：This research was financially supported in part by the Ministry of Trade,Industry and Energy(MOTIE)and Korea Institute for Advancement of Technology(KIAT)through the International Cooperative R&D program.(Project No.P0016038) in part by the MSIT(Ministry of Science and ICT),Korea,under the ITRC(Information Technology Research Center)support program(IITP-2021-2016-0-00312)supervised by the IITP(Institute for Information&communications Technology Planning&Evaluation) 

主　　题：Fault prediction association rule data mining frequent pattern mining 

摘      要：Despite advances in technological complexity and efforts,software repository maintenance requires reusing the data to reduce the effort and ***,increasing ambiguity,irrelevance,and bugs while extracting similar data during software development generate a large amount of data from those data that reside in ***,there is a need for a repository mining technique for relevant and bug-free data *** paper proposes a fault prediction approach using a data-mining technique to find good predictors for high-quality *** predict errors in mining data,the Apriori algorithm was used to discover association rules by fixing confidence at more than 40%and support at least 30%.The pruning strategy was adopted based on evaluation ***,the rules were extracted from three projects of different domains;the extracted rules were then combined to obtain the most popular rules based on the evaluation measure *** evaluate the proposed approach,we conducted an experimental study to compare the proposed rules with existing ones using four different industrial *** evaluation showed that the results of our proposal are *** and developers can utilize these rules for defect prediction during early software development.