Correlating software metrics with software defects

被引:3
作者
Korpalski, Maciej [1 ]
Sosnowski, Janusz [1 ]
机构
[1] Warsaw Univ Technol, Inst Comp Sci, Ul Nowowiejska 15-19, PL-00665 Warsaw, Poland
来源
PHOTONICS APPLICATIONS IN ASTRONOMY, COMMUNICATIONS, INDUSTRY, AND HIGH-ENERGY PHYSICS EXPERIMENTS 2018 | 2018年 / 10808卷
关键词
Software metrics; defect prediction; software reliability; empirical study; EMPIRICAL VALIDATION; SELECTION; PREDICTION;
D O I
10.1117/12.2501150
中图分类号
O43 [光学];
学科分类号
070207 ; 0803 ;
摘要
In software development and testing an interesting issue is checking correlations of observed software defects with various product and process metrics. Such analysis is helpful in predicting potential defects and optimization of testing processes. In the paper we present results of deeper studies in this area, they involve many metrics and various prediction schemes taking into account diverse correlation parameters. Special attention is paid to the problem of selecting most significant metrics. In the prediction schemes we consider modified and non modified program objects. The presented analysis methods have been verified in an experimental investigation covering twelve open source projects, for some of them several subsequent versions have been examined This is followed by result discussion.
引用
收藏
页数:11
相关论文
共 18 条
[1]  
Basili L. V., 1996, IEEE T SOFTWARE ENG, V22
[2]   Selection of Metrics for the Defect Prediction [J].
Bluemke, Ilona ;
Stepien, Anna .
DEPENDABILITY ENGINEERING AND COMPLEX SYSTEMS, 2016, 470 :39-50
[3]   SMOTE: Synthetic minority over-sampling technique [J].
Chawla, Nitesh V. ;
Bowyer, Kevin W. ;
Hall, Lawrence O. ;
Kegelmeyer, W. Philip .
2002, American Association for Artificial Intelligence (16)
[4]   Choosing software metrics for defect prediction: an investigation on feature selection techniques [J].
Gao, Kehan ;
Khoshgoftaar, Taghi M. ;
Wang, Huanjing ;
Seliya, Naeem .
SOFTWARE-PRACTICE & EXPERIENCE, 2011, 41 (05) :579-606
[5]   Empirical validation of object-oriented metrics on open source software for fault prediction [J].
Gyimóthy, T ;
Ferenc, R ;
Siket, I .
IEEE TRANSACTIONS ON SOFTWARE ENGINEERING, 2005, 31 (10) :897-910
[6]   Benchmarking attribute selection techniques for discrete class data mining [J].
Hall, MA ;
Holmes, G .
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, 2003, 15 (06) :1437-1447
[7]   Investigating software testing and maintenance reports: Case study [J].
Janczarek, Pawel ;
Sosnowski, Janusz .
INFORMATION AND SOFTWARE TECHNOLOGY, 2015, 58 :272-288
[8]  
Khamis N., 2010, P NATURAL LANGUAGE P
[9]   Which process metrics can significantly improve defect prediction models? An empirical study [J].
Madeyski, Lech ;
Jureczko, Marian .
SOFTWARE QUALITY JOURNAL, 2015, 23 (03) :393-422
[10]   Empirical validation of three software metrics suites to predict fault-proneness of object-oriented classes developed using highly iterative or agile software development processes [J].
Olague, Hector M. ;
Etzkorn, Letha H. ;
Gholston, Sampson ;
Quattlebaum, Stephen .
IEEE TRANSACTIONS ON SOFTWARE ENGINEERING, 2007, 33 (06) :402-419