메뉴 건너뛰기




Volumn 83, Issue 5, 2010, Pages 868-882

A symbolic fault-prediction model based on multiobjective particle swarm optimization

Author keywords

Fault prediction; Multiobjective; Particle swarm optimization; Rule learning algorithm

Indexed keywords

AREA UNDER THE ROC CURVE; DATA SETS; DESIGN METRICS; FAULT PREDICTION; MACHINE-LEARNING; MULTI OBJECTIVE PARTICLE SWARM OPTIMIZATION; PARETO DOMINANCE; PREDICTION MODEL; UNBALANCED DATA;

EID: 77549086028     PISSN: 01641212     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jss.2009.12.023     Document Type: Article
Times cited : (95)

References (45)
  • 1
    • 0346896353 scopus 로고    scopus 로고
    • An empirical validation of object-oriented metrics in two different iterative software processes
    • Alshayeb M., and Li W. An empirical validation of object-oriented metrics in two different iterative software processes. IEEE Transaction on Software Engineering 29 11 (2003) 1043-1049
    • (2003) IEEE Transaction on Software Engineering , vol.29 , Issue.11 , pp. 1043-1049
    • Alshayeb, M.1    Li, W.2
  • 2
    • 38049061698 scopus 로고    scopus 로고
    • Baronti, F., Starita, A., 2007. Hypothesis Testing with Classifier Systems for Rule-Based Risk Prediction, EvoBIO, pp. 24-34. doi:10.1007/978-3-540-71783-6_3.
    • Baronti, F., Starita, A., 2007. Hypothesis Testing with Classifier Systems for Rule-Based Risk Prediction, EvoBIO, pp. 24-34. doi:10.1007/978-3-540-71783-6_3.
  • 4
    • 0031191630 scopus 로고    scopus 로고
    • The use of the area under the ROC curve in the evaluation of machine learning algorithms
    • Bradley A.P. The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern Recognition 30 7 (1997) 1145-1159
    • (1997) Pattern Recognition , vol.30 , Issue.7 , pp. 1145-1159
    • Bradley, A.P.1
  • 6
    • 0343280011 scopus 로고    scopus 로고
    • Exploring the relationships between design measures and software quality in object-oriented systems
    • Briand L.C., Wust J., Daly J.W., and Porter D.V. Exploring the relationships between design measures and software quality in object-oriented systems. Journal of Systems and Software 51 3 (2000) 245-273
    • (2000) Journal of Systems and Software , vol.51 , Issue.3 , pp. 245-273
    • Briand, L.C.1    Wust, J.2    Daly, J.W.3    Porter, D.V.4
  • 10
    • 29644438050 scopus 로고    scopus 로고
    • Statistical comparisons of classifiers over multiple data sets
    • Demšar J. Statistical comparisons of classifiers over multiple data sets. Journal of Machine Learning Research 7 (2006) 1-30
    • (2006) Journal of Machine Learning Research , vol.7 , pp. 1-30
    • Demšar, J.1
  • 12
    • 40749135790 scopus 로고    scopus 로고
    • Predicting defect-prone software modules using support vector machines
    • Elish K.O., and Elish M.O. Predicting defect-prone software modules using support vector machines. Journal of Systems and Software 81 5 (2008) 649-660
    • (2008) Journal of Systems and Software , vol.81 , Issue.5 , pp. 649-660
    • Elish, K.O.1    Elish, M.O.2
  • 13
    • 78149315656 scopus 로고    scopus 로고
    • Using rule sets to maximize ROC performance
    • IEEE Computer Society, pp
    • Fawcett, T., 2001. Using rule sets to maximize ROC performance. In: IEEE International Conference on Data Mining. IEEE Computer Society, pp. 131-138.
    • (2001) IEEE International Conference on Data Mining , pp. 131-138
    • Fawcett, T.1
  • 14
    • 78149315656 scopus 로고    scopus 로고
    • Using rule sets to maximize ROC performance
    • Cercone N., Lin T.Y., and Wu X. (Eds), IEEE Computer Society
    • Fawcett T. Using rule sets to maximize ROC performance. In: Cercone N., Lin T.Y., and Wu X. (Eds). ICDM (2001), IEEE Computer Society 131-138
    • (2001) ICDM , pp. 131-138
    • Fawcett, T.1
  • 16
    • 77549088011 scopus 로고    scopus 로고
    • Ferri, C., Flach, P., Hernandez-Orallo, J., 2002. Learning decision trees using the area under the ROC curve. In: Sammut, C., Hoffmann, A. (Eds.), Proceedings of the 19th International Conference on Machine Learning. Morgan Kaufmann, pp. 139-146. %3chttp://www.cs.bris.ac.uk/Publications/Papers/1000687.pdf%3e.
    • Ferri, C., Flach, P., Hernandez-Orallo, J., 2002. Learning decision trees using the area under the ROC curve. In: Sammut, C., Hoffmann, A. (Eds.), Proceedings of the 19th International Conference on Machine Learning. Morgan Kaufmann, pp. 139-146. %3chttp://www.cs.bris.ac.uk/Publications/Papers/1000687.pdf%3e.
  • 17
    • 36849025506 scopus 로고    scopus 로고
    • Applying machine learning to software fault-proneness prediction
    • Gondra I. Applying machine learning to software fault-proneness prediction. Journal of Systems and Software 81 2 (2008) 186-195. %3chttp://www.dx.doi.org/10.1016/j.jss.2007.05.035%3e
    • (2008) Journal of Systems and Software , vol.81 , Issue.2
    • Gondra, I.1
  • 23
    • 0004222346 scopus 로고    scopus 로고
    • Morgan Kaufmann Publishers Inc., San Francisco, CA, USA
    • Kennedy J., and Eberhart R. Swarm Intelligence (2001), Morgan Kaufmann Publishers Inc., San Francisco, CA, USA
    • (2001) Swarm Intelligence
    • Kennedy, J.1    Eberhart, R.2
  • 26
    • 49349089233 scopus 로고    scopus 로고
    • Benchmarking classification models for software defect prediction: a proposed framework and novel findings
    • Lessmann S., Baesens B., Mues C., and Pietsch S. Benchmarking classification models for software defect prediction: a proposed framework and novel findings. IEEE Transaction on Software Engineering 34 4 (2008) 485-496
    • (2008) IEEE Transaction on Software Engineering , vol.34 , Issue.4 , pp. 485-496
    • Lessmann, S.1    Baesens, B.2    Mues, C.3    Pietsch, S.4
  • 32
    • 34648835118 scopus 로고    scopus 로고
    • Empirical analysis of software fault content and fault proneness using bayesian methods
    • Pai G.J., and Dugan J.B. Empirical analysis of software fault content and fault proneness using bayesian methods. IEEE Transaction on Software Engineering 33 10 (2007) 675-686
    • (2007) IEEE Transaction on Software Engineering , vol.33 , Issue.10 , pp. 675-686
    • Pai, G.J.1    Dugan, J.B.2
  • 33
    • 33748325940 scopus 로고    scopus 로고
    • Improving fault prediction using bayesian networks for the development of embedded software applications: research articles
    • Prez-Miana E., and Gras J.-J. Improving fault prediction using bayesian networks for the development of embedded software applications: research articles. Software, Testing, Verification and Reliability 16 3 (2006) 157-174
    • (2006) Software, Testing, Verification and Reliability , vol.16 , Issue.3 , pp. 157-174
    • Prez-Miana, E.1    Gras, J.-J.2
  • 35
    • 0035283313 scopus 로고    scopus 로고
    • Robust classification for imprecise environments
    • Provost F., and Fawcett T. Robust classification for imprecise environments. Machine Learning 42 3 (2001) 203
    • (2001) Machine Learning , vol.42 , Issue.3 , pp. 203
    • Provost, F.1    Fawcett, T.2
  • 36
    • 0002900357 scopus 로고    scopus 로고
    • The case against accuracy estimation for comparing induction algorithms
    • Morgan Kaufmann, San Francisco, CA, pp
    • Provost, F., Fawcett, T., Kohavi, R., 1998. The case against accuracy estimation for comparing induction algorithms. In: Proceedings 15th International Conference on Machine Learning. Morgan Kaufmann, San Francisco, CA, pp. 445-453.
    • (1998) Proceedings 15th International Conference on Machine Learning , pp. 445-453
    • Provost, F.1    Fawcett, T.2    Kohavi, R.3
  • 38
    • 77549086590 scopus 로고    scopus 로고
    • Rakotomamonjy, A., 2004. Optimizing area under roc curve with SVMs. In: Hernández-Orallo, J., Ferri, C., Lachiche, N., Flach, P.A. (Eds.), ROCAI, pp. 71-80.
    • Rakotomamonjy, A., 2004. Optimizing area under roc curve with SVMs. In: Hernández-Orallo, J., Ferri, C., Lachiche, N., Flach, P.A. (Eds.), ROCAI, pp. 71-80.
  • 40
    • 77549088399 scopus 로고    scopus 로고
    • Software fault pronennes prediction using support vector machines
    • IEEE Computer Society, London, UK
    • Singh, Y., Kaur, A., Malhotra, R., 2009. Software fault pronennes prediction using support vector machines. In: Proceedings of the World Congress of Engineering 2009. IEEE Computer Society, London, UK.
    • (2009) Proceedings of the World Congress of Engineering
    • Singh, Y.1    Kaur, A.2    Malhotra, R.3
  • 41
    • 11144262148 scopus 로고    scopus 로고
    • Application of neural networks for software quality prediction using object-oriented metrics
    • Thwin M.M.T., and Quah T.-S. Application of neural networks for software quality prediction using object-oriented metrics. Journal of Systems and Software 76 2 (2005) 147-156
    • (2005) Journal of Systems and Software , vol.76 , Issue.2 , pp. 147-156
    • Thwin, M.M.T.1    Quah, T.-S.2
  • 42
    • 40749154789 scopus 로고    scopus 로고
    • Mining software repositories for comprehensible software fault prediction models
    • Vandecruys O., Martens D., Baesens B., Mues C., Backer M.D., and Haesen R. Mining software repositories for comprehensible software fault prediction models. Journal of Systems and Software 81 5 (2008) 823-839. %3chttp://www.dx.doi.org/10.1016/j.jss.2007.07.034%3e
    • (2008) Journal of Systems and Software , vol.81 , Issue.5 , pp. 823-839
    • Vandecruys, O.1    Martens, D.2    Baesens, B.3    Mues, C.4    Backer, M.D.5    Haesen, R.6
  • 43
    • 33750954007 scopus 로고    scopus 로고
    • A novel method for early software quality prediction based on support vector machine
    • IEEE Computer Society, Washington, DC, USA, pp
    • Xing, F., Guo, P., Lyu, M.R., 2005. A novel method for early software quality prediction based on support vector machine. In: ISSRE '05: Proceedings of the 16th IEEE International Symposium on Software Reliability Engineering. IEEE Computer Society, Washington, DC, USA, pp. 213-222. http://www.dx.doi.org/10.1109/ISSRE.2005.6.
    • (2005) ISSRE '05: Proceedings of the 16th IEEE International Symposium on Software Reliability Engineering , pp. 213-222
    • Xing, F.1    Guo, P.2    Lyu, M.R.3
  • 45
    • 33947174112 scopus 로고    scopus 로고
    • Empirical analysis of object-oriented design metrics for predicting high and low severity faults
    • Zhou Y., and Leung H. Empirical analysis of object-oriented design metrics for predicting high and low severity faults. IEEE Transaction on Software Engineering 32 10 (2006) 771-789
    • (2006) IEEE Transaction on Software Engineering , vol.32 , Issue.10 , pp. 771-789
    • Zhou, Y.1    Leung, H.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.