Evaluating the change of software fault behavior with dataset attributes based on categorical correlation

Advances in Engineering Software

Volumn 42, Issue 8, 2011, Pages 535-546

  Alsmadi, Izzat ^a   Najadat, Hassan ^b  

^a YARMOUK UNIVERSITY   (Jordan)

^b JORDAN UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Jordan)

Author keywords

Clustering; Correlation; Data mining; Fault prone modules; Prediction algorithms; Software mining; Software quality

Indexed keywords

ALGORITHMS; COMPUTER SOFTWARE SELECTION AND EVALUATION; DATA MINING; DECISION MAKING; DEFECTS; FORECASTING; LAKES; RATING; SOFTWARE ENGINEERING;

ATTRIBUTE VALUES; CATEGORICAL VARIABLES; CLASS PREDICTION; CLUSTERING; COST ESTIMATIONS; DATA MINING ALGORITHM; DATA SETS; FAULT-PRONE MODULES; HISTORICAL DATA; IF THERE ARE; OTHER ALGORITHMS; PREDICTION ALGORITHMS; PREDICTION CAPABILITY; PREDICTION PERFORMANCE; PRODUCT ATTRIBUTES; RESEARCH PAPERS; SOFTWARE FAULT; SOFTWARE QUALITY;

DATA PROCESSING;

EID: 79958251975     PISSN: 09659978     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.advengsoft.2011.03.010     Document Type: Article

References (26)

1. Huzefa Kagdi, Michael L. Collard, and Jonathan I. Maletic A survey and taxonomy of approaches for mining software repositories in the context of software evolution J Softw Main Evol: Res Practice 2007
2. Jiawi Han, and Micheline Kamber Data mining concepts and techniques 2nd ed. 2006 Morgan Kaufman Publishers
3. Oliver Vanderuys, David Martens, Bart Baesens, Christophe Mues, Manu De Backer, and Raf Haesen Mining software repositories for comprehensible software fault prediction models J Syst Softw 81 2008 823 839 (Pubitemid 351389560)
4. Tim Menzies, Jeremy Greenwald, and Art Frank Data mining static code attributes to learn defect predictors IEEE Trans Softw Eng 33 1 2007 [January]
5. Menzies T, DiStefano J, Orrego A, Chapman R. Assessing predictors of software defects. In: Proc workshop predictive software models; 2004.
6. M. Halstead Elements of software science 1977 Elsevier
7. T. McCabe A complexity measure IEEE Trans Softw Eng 2 4 1976 308 320
8. Menzies T, DiStefeno JS, Chapman M, Mcgill K. Metrics that matter. In: Proc 27th NASA SEL workshop software eng; 2002.
9. Nagappan N, Ball T. Static analysis tools as early indicators of pre-release defect density. In: Proc int'l conf software eng; 2005.
10. Menzes T, Turhan B, Bener A, Gay G, Cukic B, Jiang Y. Implications of ceiling effects in defect predictors. PROMISE, submitted for publication.
11. Shull F, Basili V, Boehm B, Brown A, Costa P, Lindvall M, et al. What we have learned about fighting defect. In: Proceedings of 8th international software metrics symposium, Ottawa, Canada; 2002. p. 249-58. < http://fc-md.umd.edu/fcmd/Papers/shulldefects.ps >.
12. Menzies T, Raffo D, on Setamanit S, Hu Y, Tootoonian S. Model-based tests of truisms. In: Proceedings of IEEE ASE 2002; 2002. .
13. Jiang Yue, Cukic Bojan, Menzies Tim, Bartlow Nick. Comparing design and code metrics for software quality prediction. Morgantown: The Lane Department of Computer Science and Electrical Engineering West Virginia University.
14. Zimmermann T, Premraj R, Zeller A. Predicting defects for eclipse. In: PROMISE'07: international workshop on ICSE workshops 2007; May 2007. p. 9-9.
15. Holte Robert C. Very simple classification rules perform well on most commonly used datasets. University of Ottawa, Ottawa, Canada K1N 6N5.
16. T.J. Ostrand, and E.J. Weyuker Collecting and categorizing software error data in an industrial environment J Syst Softw 300 1984 289
17. Ostrand Thomas J, Weyuker Elaine J. The distribution of faults in a large industrial software system. In: Proceedings of the 2002 ACM SIGSOFT international symposium on software testing and analysis. ACM Press; 2002. p. 55-64.
18. Carina Andersson, and Per Runeson A replicated quantitative analysis of fault distributions in complex software systems IEEE Trans Softw Eng 33 5 2007
19. N.E. Fenton, and N. Ohlsson Quantitative analysis of faults and failures in a complex software system IEEE Trans Softw Eng 26 8 2000 797 814
20. de Carvalho Andre B, Pozo Aurora, Vergilio Silvia, Lenz Alexandre. Predicting fault proneness of classes trough a multiobjective particle swarm optimization algorithm. In: 20th IEEE international conference on tools with artificial intelligence; 2008.
21. Castro Cristiano Leite, Braga Antonio Padua. Optimization of the area under the ROC curve. In: 10th Brazilian symposium on neural networks; 2008.
22. Andrew Bradley The use of the area under the ROC curve in the evaluation of machine learning algorithms Pattern Recogn 30 7 1997 1145 1159 (Pubitemid 127406521)
23. J.P. Egan Signal detection theory and ROC analysis 1975 Academic Press
24. Hingyu Zhang, and Xiuzhen Zhang Comments on data mining static code attributes to learn defect predictors IEEE Trans Softw Eng 33 9 2007 [September]
25. Tim Menzies, Alex Dekhtyar, Justin Distefano, and Jeremy Greenwald Problems with precision: a response to "Comments on Data Mining Static Code Attributes to Learn Defect Predictors" IEEE Trans Softw Eng 33 2007 9 September
26. N.E. Fenton, and S. Pfleeger Software metrics: a rigorous and practical approach 1997 International Thompson Press