Computer Science ›› 2016, Vol. 43 ›› Issue (7): 171-176.doi: 10.11896/j.issn.1002-137X.2016.07.031

Previous Articles     Next Articles

Software Defect Prediction Model Based on GMDH Causal Relationship

ZHANG De-ping, LIU Guo-qiang and ZHANG Ke   

  • Online:2018-12-01 Published:2018-12-01

Abstract: Software defect prediction is an important aspect in the field of software reliability research.In this paper,we presented a software defect prediction model based on GMDH networks and causal test theory.The model selects the software metrics with the defect causal relationship by learning Granger test ideas and uses the GMDH network which can check the non-linear causality between multiple factors of software defect.Finally,based on two real software failure data sets,we designed an experiment to compare the proposed method with the Granger test software defect prediction model.The experiment results show that the proposed model is more effective and efficient than Granger test software defect prediction model.

Key words: Software defect,Causal relationship,Software metrics,GMDH network,Granger test

[1] Wang Qing,Wu Shu-jian,Li Ming-shu.Software defect predic-tion technology [J].Journal of Software,2008,19(7):1565-1580(in Chinese) 王青,伍书剑,李明树.软件缺陷预测技术[J].软件学报,2008,19(7):1565-1580
[2] Nagappan N,Ball T,Zeller A.Mining metrics to predict component failures[C]∥28th International Conference on Software Engineering (ICSE).2006:452-461
[3] Couto C,Araujo J E,Silva C,et al.Static correspondence and cor-relation between field defects and warnings reported by a bug finding tool[J].Software Quality Journal,2013,21(2):241-257
[4] Catal C.Software fault prediction:A literature review and current trends[J].Expert Systems with Applications,2011,38(4):4626-4636
[5] Singh Y,Kaur A,Malhotra R.Empirical validation of object-oriented metrics for predicting fault proneness models[J].Software Quality Journal,2010,18(1):3-35
[6] Couto C,Montandon J E,Silva C,et al.Static correspondenceand correlation between field defects and warnings reported by a bug finding tool[J].Software Quality Journal,2013,21(2):241-257
[7] Zimmermann T,Nagappan N.Predicting defects using networkanalysis on dependency graphs[C]∥Proceedings of the 30th International Conference on Software Engineering.ACM,2008:531-540
[8] Nagappn N,Ball T.Using software dependencies and churn me-trics to predict field failures:an empirical case study[C]∥First International Symposisum on Empirical Software Engineering and Measurement.2007:364-373
[9] Lee H J,Naish L,Ramamohanarao K.Study of the relationship of bug consistency with respect to performance of spectra me-trics[C]∥2nd IEEE International Conference on Computer Science and Information Technology,2009(ICCSIT 2009).2009:501-508
[10] Okutan A,Yildiz O T.Software defect prediction using Bayesian networks[J].Empir Software Eng.,2014,19(3):154-181
[11] Lehtinen T O A,Mntyl M V,Vanhanen J,et al.Perceivedcauses of software project failures-An analysis of their relationships[J].Information and Software Technology,2014,56(6):623-643
[12] Couto C,Silva C,Valente M T,et al.Uncovering causal relation-ships between software metrics and bugs[C]∥17th European Conference on Software Maintenance and Reengineering (CSMR).2012:223-232
[13] Couto C,Pires P,Valente M T,et al.Predicting software defects with causality tests[J].Journal of Systems and Software,2014,93(6):24-41
[14] D’Ambros M,Lanza M,Robbes R.An extensive comparison of bug prediction approaches[C]∥7th IEEE Working Conference on Mining Software Repositories (MSR),2010.2010:31-41
[15] Granger C.Investigating causal relations by econometric models and cross-spectral methods[J].Econometrica,1969,37(3):424-438
[16] Chow G C.Econometrics[M].New York:McGraw Hill,1983
[17] Zhang M Z,He C Z,Gu X,et al.D-GMDH:A novel inductive modelling approach in the forecasting of the industrial economy[J].Economic Modelling,2013,30(2):514-520
[18] Tamura H,Kondo T.Heuristics Free Group Method of DataHandling Algorithm of Generating Optimal Partial Polynomials with Application to Air Pollution Prediction[J].International Journal of Systems Science,1980,11(9):1095-1011
[19] Liu W,Tian S B.An Improved GMSM Method and its Application[J].ACTA Automatic Sinica,1993,19(4):468-471
[20] Ramakanta M,Ravi V.Software Reliability Prediction UsingGroup Method of Data Handling[C]∥12th International Conference of Rough Sets,Fuzzy Sets,Data Mining and Granular Computing.2009:13-19
[21] Couto C,Piresa P.Predicting software defects with causalitytests[J].Empirical Software Engineering,2014,19:154-181

No related articles found!
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
[1] LEI Li-hui and WANG Jing. Parallelization of LTL Model Checking Based on Possibility Measure[J]. Computer Science, 2018, 45(4): 71 -75, 88 .
[2] XIA Qing-xun and ZHUANG Yi. Remote Attestation Mechanism Based on Locality Principle[J]. Computer Science, 2018, 45(4): 148 -151, 162 .
[3] LI Bai-shen, LI Ling-zhi, SUN Yong and ZHU Yan-qin. Intranet Defense Algorithm Based on Pseudo Boosting Decision Tree[J]. Computer Science, 2018, 45(4): 157 -162 .
[4] WANG Huan, ZHANG Yun-feng and ZHANG Yan. Rapid Decision Method for Repairing Sequence Based on CFDs[J]. Computer Science, 2018, 45(3): 311 -316 .
[5] SUN Qi, JIN Yan, HE Kun and XU Ling-xuan. Hybrid Evolutionary Algorithm for Solving Mixed Capacitated General Routing Problem[J]. Computer Science, 2018, 45(4): 76 -82 .
[6] ZHANG Jia-nan and XIAO Ming-yu. Approximation Algorithm for Weighted Mixed Domination Problem[J]. Computer Science, 2018, 45(4): 83 -88 .
[7] WU Jian-hui, HUANG Zhong-xiang, LI Wu, WU Jian-hui, PENG Xin and ZHANG Sheng. Robustness Optimization of Sequence Decision in Urban Road Construction[J]. Computer Science, 2018, 45(4): 89 -93 .
[8] LIU Qin. Study on Data Quality Based on Constraint in Computer Forensics[J]. Computer Science, 2018, 45(4): 169 -172 .
[9] ZHONG Fei and YANG Bin. License Plate Detection Based on Principal Component Analysis Network[J]. Computer Science, 2018, 45(3): 268 -273 .
[10] SHI Wen-jun, WU Ji-gang and LUO Yu-chun. Fast and Efficient Scheduling Algorithms for Mobile Cloud Offloading[J]. Computer Science, 2018, 45(4): 94 -99, 116 .