Empirical analysis of network measures for predicting high severity software faults

Zhou Y, Leung H. Empirical analysis of object-oriented design metrics for predicting high and low severity faults. IEEE Trans Softw Eng, 2006, 32: 771–789

Chhillar R S, Nisha. Empirical analysis of object-oriented design metrics for predicting high, medium and low severity faults using mallows Cp. ACM SIGSOFT Softw Eng Notes, 2011, 36: 1–9

Basili V R, Briand L C, Melo W L. A validation of object-oriented design metrics as quality indicators. IEEE Trans Softw Eng, 1996, 22: 751–761

Subramanyam R, Krishnan M S. Empirical analysis of ck metrics for object-oriented design complexity: implications for software defects. IEEE Trans Softw Eng, 2003, 29: 297–310

Nagappan N, Ball T, Zeller A. Mining metrics to predict component failures. In: Proceedings of the 28th International Conference on Software Engineering. New York: ACM, 2006. 452–461

Zhang H Y. An investigation of the relationships between lines of code and defects. In: Proceedings of 2009 IEEE International Conference on Software Maintenance. Piscataway: IEEE, 2009. 274–283

Nagappan N, Ball T. Use of relative code churn measures to predict system defect density. In: Proceedings of the 27th International Conference on Software Engineering. Piscataway: IEEE, 2005. 284–292

Moser R, Pedrycz W, Succi G. A comparative analysis of the efficiency of change metrics and static code attributes for defect prediction. In: Proceedings of the 30th International Conference on Software Engineering. Piscataway: IEEE, 2008. 181–190

Hassan A E. Predicting faults using the complexity of code changes. In: Proceedings of the 31st International Conference on Software Engineering. Piscataway: IEEE, 2009. 78–88

Hassan A E, Holt R C. The top ten list: dynamic fault prediction. In: Proceedings of the 21st IEEE International Conference on Software Maintenance. Piscataway: IEEE, 2005. 263–272

Ostrand T J, Weyuker E J, Bell R M. Predicting the location and number of faults in large software systems. IEEE Trans Softw Eng, 2005, 31: 340–355

Zhang W Q, Nie L M, Jiang H, et al. Developer social networks in software engineering: construction, analysis, and applications. Sci China Inf Sci, 2014, 57: 121101

Zimmermann T, Nagappan N. Predicting defects with program dependencies. In: Proceedings of the 3rd International Symposium on Empirical Software Engineering and Measurement. Piscataway: IEEE, 2009: 435–438

Zimmermann T, Nagappan N. Predicting defects using network analysis on dependency graphs. In: Proceedings of the 30th International Conference on Software Engineering. New York: ACM, 2008. 531–540

Taba S E S, Khomh F, Zou Y, et al. Predicting bugs using antipatterns. In: Proceedings of the 29th IEEE International Conference on Software Maintenance. Piscataway: IEEE, 2013. 270–279

Tosun A, Turhan B, Bener A. Validation of network measures as indicators of defective modules in software systems. In: Proceedings of the 5th International Conference on Predictor Models in Software Engineering. New York: ACM, 2009. 1–5

Premraj R, Herzig K. Network versus code metrics to predict defects: a replication study. In: Proceedings of the 5th International Symposium on Empirical Software Engineering and Measurement. Piscataway: IEEE, 2011. 215–224

Nguyen T H D, Adams B, Hassan A E. Studying the impact of dependency network measures on software quality. In: Proceedings of the 2010 IEEE International Conference on Software Maintenance. Piscataway: IEEE, 2010. 1–10

Ma Y, He K, Li B, et al. How multiple-dependency structure of classes affects their functions a statistical perspective. In: Proceedings of the 2nd International Conference on Software Technology and Engineering. Piscataway: IEEE, 2010, v2: 60–66

Basili V R, Shull F, Lanubile F. Building knowledge through families of experiments. IEEE Trans Softw Eng, 1999, 25: 456–473

Halstead M H. Elements of Software Science. New York: Elsevier, 1977. 50–70

Chidamber S R, Kemerer C F. A metrics suite for object oriented design. IEEE Trans Softw Eng, 1994, 20: 476–493

Hosmer Jr D W, Lemeshow S. Applied Logistic Regression. New Jersey: John Wiley & Sons, 2004. 153–223

Belsley D A, Kuh E, Welsch R E. Regression Diagnostics: Identifying Influential Data and Sources of Collinearity. New Jersey: John Wiley & Sons, 2005. 6–38

Harrell F E. Regression Modeling Strategies: With Applications to Linear Models, Logistic Regression, and Survival Analysis. New York: Springer, 2001. 215–268

Kutner M H, Nachtsheim C, Neter J. Applied Linear Regression Models. 4th ed. Chicago: Irwin, 2004. 20–70

Maddala G S. Limited-Dependent and Qualitative Variables in Econometrics. New York: Cambridge University Press, 1983. 100–124

Nagelkerke N J D. A note on a general definition of the coefficient of determination. Biometrika, 1991, 78: 691–692

Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J Royal Stat Soc Ser B (Methodological), 1995, 57: 289–300

Freeman E A, Moisen G G. A comparison of the performance of threshold criteria for binary classification in terms of predicted prevalence and kappa. Ecol Model, 2008, 217: 48–58

He Z, Shu F, Yang Y, et al. An investigation on the feasibility of cross-project defect prediction. Autom Softw Eng, 2012, 19: 167–199

Chang R H, Mu X D, Zhang L. Software defect prediction using non-negative matrix factorization. J Softw, 2011, 6: 2114–2120

Yin R K. Case Study Research: Design and Methods. 3rd ed. New York: SAGE Publications, 2002. 120–180

Kim S, Zhang H, Wu R, et al. Dealing with noise in defect prediction. In: Proceedings of the 33rd International Conference on Software Engineering. Piscataway: IEEE, 2011. 481–490

Zhou Y, Xu B, Leung H. On the ability of complexity metrics to predict fault-prone classes in object-oriented systems. J Syst Softw, 2010, 83: 660–674

Zhou Y, Leung H, Xu B. Examining the potentially confounding effect of class size on the associations between object-oriented Metrics and change-proneness. IEEE Trans Softw Eng, 2009, 35: 607–623

Pan K, Kim S, Whitehead E J. Bug classification using program slicing metrics. In: Proceedings of the 6th International Working Conference on Source Code Analysis and Manipulation, Philadelphia, 2006. 31–42

Koru A G, Tian J. Comparing high-change modules and modules with the highest measurement values in two large-scale open-source products. IEEE Trans Softw Eng, 2005, 31: 625–642

Menzies T, Greenwald J, Frank A. Data mining static code attributes to learn defect predictors. IEEE Trans Softw Eng, 2007, 33: 2–13

Singh Y, Kaur A, Malhotra R. Empirical validation of object-oriented metrics for predicting fault proneness models. Softw Qual J, 2010, 18: 3–35

Shatnawi R, Li W. The effectiveness of software metrics in identifying fault-prone classes in post-release software evolution process. J Syst Softw, 2008, 81: 1868–1882