|
J. Ba and S. Wu, "SdDirM: A dynamic defect prediction model," in Ieee/asme International Conference on Mechatronics and Embedded Systems and Applications, 2012, pp. 252-256.
|
|
C. M. Bishop, Pattern Recognition and Machine Learning (Information Science and Statistics): Springer-Verlag New York, Inc., 2006.
|
|
G. Boetticher, T. Menzies, T. Ostrand, and G. Boetticher, "\{PROMISE\} Repository of empirical software engineering data," West Virginia University Department of Computer Science, 2007.
|
|
S. Chamoli, G. Tenne, and S. Bhatia, "Analysing Software Metrics for Accurate Dynamic Defect Prediction Models," Indian Journal of Science & Technology, vol. 8, 2015.
|
|
S. S. Choi, S. H. Cha, and C. C. Tappert, "A Survey of Binary Similarity and Distance Measures," Journal of Systemics Cybernetics & Informatics, vol. 8, pp. 43--48, 2009.
|
|
K. Gao, T. M. Khoshgoftaar, H. Wang, and N. Seliya, "Choosing software metrics for defect prediction: an investigation on feature selection techniques," Software Practice & Experience, vol. 41, pp. 579–606, 2011.
|
|
C. W. J. Granger, "Investigating Causal Relations by Econometric Models and Cross-spectral Methods," Econometrica, vol. 37, pp. 424-438, 1969.
|
|
H. He and E. A. Garcia, "Learning from Imbalanced Data," Knowledge & Data Engineering IEEE Transactions on, vol. 21, pp. 1263-1284, 2009.
|
|
P. He, B. Li, X. Liu, J. Chen, and Y. Ma, "An empirical study on software defect prediction with a simplified metric set," Information & Software Technology, vol. 59, pp. 170-190, 2015.
|
|
Z. He, F. Shu, Y. Yang, M. Li, and Q. Wang, "An investigation on the feasibility of cross-project defect prediction," Automated Software Engineering, vol. 19, pp. 167-199, 2012.
|
|
K. Herzig, S. Just, and A. Zeller, "It's not a bug, it's a feature: How misclassification impacts bug prediction," in International Conference on Software Engineering, 2013, pp. 392-401.
|
|
Y. Hong, W. Kim, and J. Joo, "Prediction of defect distribution based on project characteristics for proactive project management," in International Conference on Predictive MODELS in Software Engineering, 2010, p. 15.
|
|
G. Jagannathan, K. Pillaipakkamnatt, and R. N. Wright, "A Practical Differentially Private Random Decision Tree Classifier," in IEEE International Conference on Data Mining Workshops, 2009, pp. 114-121.
|
|
X. Y. Jing, S. Ying, Z. W. Zhang, S. S. Wu, and J. Liu, "Dictionary learning based software defect prediction," 2014, pp. 414-423.
|
|
M. Jureczko and L. Madeyski, "Towards identifying software project clusters with regard to defect prediction," in International Conference on Predictive MODELS in Software Engineering, Promise 2010, Timisoara, Romania, September, 2010, pp. 1-10.
|
|
T. M. Khoshgoftaar, K. Gao, and A. NAPOLITANO, "AN EMPIRICAL STUDY OF FEATURE RANKING TECHNIQUES FOR SOFTWARE QUALITY PREDICTION," International Journal of Software Engineering & Knowledge Engineering, vol. 22, pp. 161-183, 2012.
|
|
S. Lessmann, B. Baesens, C. Mues, and S. Pietsch, "Benchmarking Classification Models for Software Defect Prediction: A Proposed Framework and Novel Findings," IEEE Transactions on Software Engineering, vol. 34, pp. 485-496, 2008.
|
|
X. S. Liang, "Normalizing the causality between time series," Phys. Rev. E, vol. 92, 2015.
|
|
X. S. Liang, "Unraveling the cause-effect relation between time series," Physical Review E Statistical Nonlinear & Soft Matter Physics, vol. 90, p. 052150, 2014.
|
|
W. Liu, S. Liu, Q. Gu, and J. Chen, "Empirical Studies of a Two-Stage Data Preprocessing Approach for Software Fault Prediction," IEEE Transactions on Reliability, vol. 65, pp. 1-16, 2015.
|
|
T. Menzies, J. Greenwald, and A. Frank, "Data Mining Static Code Attributes to Learn Defect Predictors," IEEE Transactions on Software Engineering, vol. 33, pp. 2-13, 2007.
|
|
T. Menzies, Z. Milton, B. Turhan, B. Cukic, Y. Jiang, and A. Bener, "Defect prediction from static code features: current results, limitations, new approaches," Automated Software Engineering, vol. 17, pp. 375-407, 2010.
|
|
J. Nam and S. Kim, "Heterogeneous defect prediction," IEEE Transactions on Software Engineering, vol. PP, pp. 1-1, 2015.
|
|
F. Rahman, S. Khatri, E. T. Barr, and P. Devanbu, "Comparing static bug finders and statistical prediction," Macbeth.cs.ucdavis.edu, pp. 424-434, 2014.
|
|
Rahman, Foyzur, Posnett, Daryl, Herraiz, Devanbu, et al., "Sample size vs. bias in defect prediction," 2013.
|
|
C. Seiffert, T. M. Khoshgoftaar, J. V. Hulse, and A. Napolitano, "RUSBoost: A Hybrid Approach to Alleviating Class Imbalance," IEEE Transactions on Systems Man & Cybernetics Part A Systems & Humans, vol. 40, pp. 185-197, 2010.
|
|
M. Shepperd, Q. Song, Z. Sun, and C. Mair, "Data Quality: Some Comments on the NASA Software Defect Datasets," IEEE Transactions on Software Engineering, vol. 39, pp. 1208-1215, 2013.
|
|
S. Shivaji, E. J. Whitehead, R. Akella, and S. Kim, "Reducing Features to Improve Code Change-Based Bug Prediction," IEEE Transactions on Software Engineering, vol. 39, pp. 552-569, 2013.
|
|
Q. Song, J. Ni, and G. Wang, "A Fast Clustering-Based Feature Subset Selection Algorithm for High-Dimensional Data," Knowledge & Data Engineering IEEE Transactions on, vol. 25, pp. 1-14, 2013.
|
|
B. Turhan, T. Menzies, A. B. Bener, and J. D. Stefano, "On the relative value of cross-company and within-company data for defect prediction. Empir Softw Eng," Empirical Software Engineering, vol. 14, pp. 540-578, 2009.
|
|
J. Vaidya, M. Kantarc?o?lu, and C. Clifton, "Privacy-preserving Na?ve Bayes classification," The VLDB Journal, vol. 17, pp. 879-898, 2008.
|
|
H. Wang, T. M. Khoshgoftaar, and A. Napolitano, "A Comparative Study of Ensemble Feature Selection Techniques for Software Defect Prediction," in International Conference on Machine Learning and Applications, Icmla 2010, Washington, Dc, Usa, 12-14 December, 2010, pp. 135-140.
|
|
S. Wang and X. Yao, "Using Class Imbalance Learning for Software Defect Prediction," IEEE Transactions on Reliability, vol. 62, pp. 434-443, 2013.
|
|
F. Zhang, A. Mockus, I. Keivanloo, and Y. Zou, "Towards building a universal defect prediction model," 2014, pp. 182-191.
|