Effort-aware just-in-time defect identification in practice: a case study at Alibaba
暂无分享,去创建一个
David Lo | Ahmed E. Hassan | Meng Yan | Xin Xia | Yuanrui Fan | Xindong Zhang | Meng Yan | Xin Xia | A. Hassan | D. Lo | Yuanrui Fan | Xindong Zhang
[1] Andrew P. Bradley,et al. The use of the area under the ROC curve in the evaluation of machine learning algorithms , 1997, Pattern Recognit..
[2] Yuming Zhou,et al. How Far We Have Progressed in the Journey? An Examination of Cross-Project Defect Prediction , 2018, ACM Trans. Softw. Eng. Methodol..
[3] Osamu Mizuno,et al. Bug prediction based on fine-grained module histories , 2012, 2012 34th International Conference on Software Engineering (ICSE).
[4] Naoyasu Ubayashi,et al. An empirical study of just-in-time defect prediction using cross-project models , 2014, MSR 2014.
[5] Ahmed E. Hassan,et al. An Experience Report on Defect Modelling in Practice: Pitfalls and Challenges , 2017, 2018 IEEE/ACM 40th International Conference on Software Engineering: Software Engineering in Practice Track (ICSE-SEIP).
[6] Sashank Dara,et al. Online Defect Prediction for Imbalanced Data , 2015, 2015 IEEE/ACM 37th IEEE International Conference on Software Engineering.
[7] Yi Zhang,et al. Classifying Software Changes: Clean or Buggy? , 2008, IEEE Transactions on Software Engineering.
[8] Osamu Mizuno,et al. Training on errors experiment to detect fault-prone software modules by spam filter , 2007, ESEC-FSE '07.
[9] Shinichi Nakagawa,et al. A general and simple method for obtaining R2 from generalized linear mixed‐effects models , 2013 .
[10] Tim Menzies,et al. Revisiting unsupervised learning for defect prediction , 2017, ESEC/SIGSOFT FSE.
[11] Bart Baesens,et al. Benchmarking Classification Models for Software Defect Prediction: A Proposed Framework and Novel Findings , 2008, IEEE Transactions on Software Engineering.
[12] Ying Zou,et al. Towards just-in-time suggestions for log changes , 2016, Empirical Software Engineering.
[13] Thomas Fritz,et al. Software developers' perceptions of productivity , 2014, SIGSOFT FSE.
[14] Tom A. B. Snijders,et al. Fixed and random effects. , 2005 .
[15] Tracy Hall,et al. Researcher Bias: The Use of Machine Learning in Software Defect Prediction , 2014, IEEE Transactions on Software Engineering.
[16] N. Cliff. Ordinal methods for behavioral data analysis , 1996 .
[17] Chao Liu,et al. A two-phase transfer learning model for cross-project defect prediction , 2019, Inf. Softw. Technol..
[18] Alessandro Orso,et al. Are automated debugging techniques actually helping programmers? , 2011, ISSTA '11.
[19] Tian Jiang,et al. Personalized defect prediction , 2013, 2013 28th IEEE/ACM International Conference on Automated Software Engineering (ASE).
[20] Mollie E. Brooks,et al. Generalized linear mixed models: a practical guide for ecology and evolution. , 2009, Trends in ecology & evolution.
[21] Rainer Koschke,et al. Effort-Aware Defect Prediction Models , 2010, 2010 14th European Conference on Software Maintenance and Reengineering.
[22] David Lo,et al. Chaff from the Wheat: Characterizing and Determining Valid Bug Reports , 2020, IEEE Transactions on Software Engineering.
[23] F. Wilcoxon. Individual Comparisons by Ranking Methods , 1945 .
[24] David Lo,et al. Characterizing and identifying reverted commits , 2019, Empirical Software Engineering.
[25] Xiaoyan Zhu,et al. Does bug prediction support human developers? Findings from a Google case study , 2013, 2013 35th International Conference on Software Engineering (ICSE).
[26] Abdelwahab Hamou-Lhadj,et al. CLEVER: Combining Code Metrics with Clone Detection for Just-in-Time Fault Prevention and Resolution in Large Industrial Projects , 2018, 2018 IEEE/ACM 15th International Conference on Mining Software Repositories (MSR).
[27] Cor-Paul Bezemer,et al. Studying the dialogue between users and developers of free apps in the Google Play Store , 2017, 2018 IEEE/ACM 40th International Conference on Software Engineering (ICSE).
[28] David Lo,et al. Automating Change-Level Self-Admitted Technical Debt Determination , 2019, IEEE Transactions on Software Engineering.
[29] Frank E. Harrell,et al. Regression Modeling Strategies: With Applications to Linear Models, Logistic Regression, and Survival Analysis , 2001 .
[30] A. Scott,et al. A Cluster Analysis Method for Grouping Means in the Analysis of Variance , 1974 .
[31] Yasutaka Kamei,et al. The Impact of Using Regression Models to Build Defect Classifiers , 2017, 2017 IEEE/ACM 14th International Conference on Mining Software Repositories (MSR).
[32] Ding Yuan,et al. How do fixes become bugs? , 2011, ESEC/FSE '11.
[33] Jacek Czerwonka,et al. CRANE: Failure Prediction, Change Analysis and Test Prioritization in Practice -- Experiences from Windows , 2011, 2011 Fourth IEEE International Conference on Software Testing, Verification and Validation.
[34] Shane McIntosh,et al. Automated Parameter Optimization of Classification Techniques for Defect Prediction Models , 2016, 2016 IEEE/ACM 38th International Conference on Software Engineering (ICSE).
[35] Yuming Zhou,et al. Effort-aware just-in-time defect prediction: simple unsupervised models could be better than supervised models , 2016, SIGSOFT FSE.
[36] Sunil J Rao,et al. Regression Modeling Strategies: With Applications to Linear Models, Logistic Regression, and Survival Analysis , 2003 .
[37] Harald C. Gall,et al. Cross-project defect prediction: a large scale experiment on data vs. domain vs. process , 2009, ESEC/SIGSOFT FSE.
[38] Shane McIntosh,et al. An Empirical Comparison of Model Validation Techniques for Defect Prediction Models , 2017, IEEE Transactions on Software Engineering.
[39] Xinli Yang,et al. Deep Learning for Just-in-Time Defect Prediction , 2015, 2015 IEEE International Conference on Software Quality, Reliability and Security.
[40] Tim Menzies,et al. Data Mining Static Code Attributes to Learn Defect Predictors , 2007, IEEE Transactions on Software Engineering.
[41] Audris Mockus,et al. Predicting risk of software changes , 2000, Bell Labs Technical Journal.
[42] Shane McIntosh,et al. Are Fix-Inducing Changes a Moving Target? A Longitudinal Case Study of Just-In-Time Defect Prediction , 2018, IEEE Transactions on Software Engineering.
[43] Audris Mockus,et al. A large-scale empirical study of just-in-time quality assurance , 2013, IEEE Transactions on Software Engineering.
[44] David Lo,et al. File-Level Defect Prediction: Unsupervised vs. Supervised Models , 2017, 2017 ACM/IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM).
[45] David Lo,et al. HYDRA: Massively Compositional Model for Cross-Project Defect Prediction , 2016, IEEE Transactions on Software Engineering.
[46] Roel Bosker,et al. Multilevel analysis : an introduction to basic and advanced multilevel modeling , 1999 .
[47] Yuming Zhou,et al. Code Churn: A Neglected Metric in Effort-Aware Just-in-Time Defect Prediction , 2017, 2017 ACM/IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM).
[48] David Lo,et al. Revisiting supervised and unsupervised models for effort-aware just-in-time defect prediction , 2018, Empirical Software Engineering.
[49] Paul C. Johnson. Extension of Nakagawa & Schielzeth's R2GLMM to random slopes models , 2014, Methods in ecology and evolution.
[50] David Lo,et al. Supervised vs Unsupervised Models: A Holistic Look at Effort-Aware Just-in-Time Defect Prediction , 2017, 2017 IEEE International Conference on Software Maintenance and Evolution (ICSME).
[51] D. Stangl,et al. Encyclopedia of Statistics in Behavioral Science , 2008 .
[52] Naoyasu Ubayashi,et al. Studying just-in-time defect prediction using cross-project models , 2015, Empirical Software Engineering.
[53] Xinli Yang,et al. TLEL: A two-layer ensemble learning approach for just-in-time defect prediction , 2017, Inf. Softw. Technol..
[54] Ahmed E. Hassan,et al. An industrial study on the risk of software changes , 2012, SIGSOFT FSE.
[55] Zhenchang Xing,et al. What do developers search for on the web? , 2017, Empirical Software Engineering.
[56] Shane McIntosh,et al. The Impact of Automated Parameter Optimization on Defect Prediction Models , 2018, IEEE Transactions on Software Engineering.
[57] Andreas Zeller,et al. When do changes induce fixes? , 2005, ACM SIGSOFT Softw. Eng. Notes.
[58] Audris Mockus,et al. Towards building a universal defect prediction model , 2014, MSR 2014.
[59] David Lo,et al. Perceptions, Expectations, and Challenges in Defect Prediction , 2020, IEEE Transactions on Software Engineering.
[60] Audris Mockus,et al. How Does Context Affect the Distribution of Software Maintainability Metrics? , 2013, 2013 IEEE International Conference on Software Maintenance.