Deep Learning for Just-in-Time Defect Prediction
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Xinli Yang | David Lo | Xin Xia | Jianling Sun | Yun Zhang | Xin Xia | D. Lo | Yun Zhang | Jianling Sun | Xinli Yang
[1] Andreas Zeller,et al. Mining metrics to predict component failures , 2006, ICSE.
[2] Akito Monden,et al. Revisiting common bug prediction findings using effort-aware models , 2010, 2010 IEEE International Conference on Software Maintenance.
[3] Nachiappan Nagappan,et al. Predicting defects using network analysis on dependency graphs , 2008, 2008 ACM/IEEE 30th International Conference on Software Engineering.
[4] Ahmed E. Hassan,et al. Predicting faults using the complexity of code changes , 2009, 2009 IEEE 31st International Conference on Software Engineering.
[5] Ayse Basar Bener,et al. On the relative value of cross-company and within-company data for defect prediction , 2009, Empirical Software Engineering.
[6] Geoffrey E. Hinton,et al. Acoustic Modeling Using Deep Belief Networks , 2012, IEEE Transactions on Audio, Speech, and Language Processing.
[7] Petra Perner,et al. Data Mining - Concepts and Techniques , 2002, Künstliche Intell..
[8] Harald C. Gall,et al. Cross-project defect prediction: a large scale experiment on data vs. domain vs. process , 2009, ESEC/SIGSOFT FSE.
[9] Audris Mockus,et al. A large-scale empirical study of just-in-time quality assurance , 2013, IEEE Transactions on Software Engineering.
[10] Amy Loutfi,et al. A review of unsupervised feature learning and deep learning for time-series modeling , 2014, Pattern Recognit. Lett..
[11] N. Cliff. Ordinal methods for behavioral data analysis , 1996 .
[12] N. Nagappan,et al. Use of relative code churn measures to predict system defect density , 2005, Proceedings. 27th International Conference on Software Engineering, 2005. ICSE 2005..
[13] David Lo,et al. Evaluating defect prediction approaches using a massive set of metrics: an empirical study , 2015, SAC.
[14] Tim Menzies,et al. Better cross company defect prediction , 2013, 2013 10th Working Conference on Mining Software Repositories (MSR).
[15] Haibo He,et al. Learning from Imbalanced Data , 2009, IEEE Transactions on Knowledge and Data Engineering.
[16] Geoffrey E. Hinton. Learning multiple layers of representation , 2007, Trends in Cognitive Sciences.
[17] Premkumar T. Devanbu,et al. Sample size vs. bias in defect prediction , 2013, ESEC/FSE 2013.
[18] Taghi M. Khoshgoftaar,et al. Balancing Misclassification Rates in Classification-Tree Models of Software Quality , 2004, Empirical Software Engineering.
[19] David Lo,et al. An Empirical Study of Classifier Combination for Cross-Project Defect Prediction , 2015, 2015 IEEE 39th Annual Computer Software and Applications Conference.
[20] Sinno Jialin Pan,et al. Transfer defect learning , 2013, 2013 35th International Conference on Software Engineering (ICSE).
[21] Michele Lanza,et al. Evaluating defect prediction approaches: a benchmark and an extensive comparison , 2011, Empirical Software Engineering.
[22] Lionel C. Briand,et al. Data Mining Techniques for Building Fault-proneness Models in Telecom Java Software , 2007, The 18th IEEE International Symposium on Software Reliability (ISSRE '07).
[23] Akito Monden,et al. The Effects of Over and Under Sampling on Fault-prone Module Detection , 2007, First International Symposium on Empirical Software Engineering and Measurement (ESEM 2007).
[24] Ayse Basar Bener,et al. Defect prediction from static code features: current results, limitations, new approaches , 2010, Automated Software Engineering.
[25] Dewayne E. Perry,et al. Toward understanding the rhetoric of small source code changes , 2005, IEEE Transactions on Software Engineering.
[26] Hongfang Liu,et al. An Investigation into the Functional Form of the Size-Defect Relationship for Software Modules , 2009, IEEE Transactions on Software Engineering.
[27] Tian Jiang,et al. Personalized defect prediction , 2013, 2013 28th IEEE/ACM International Conference on Automated Software Engineering (ASE).
[28] Yee Whye Teh,et al. A Fast Learning Algorithm for Deep Belief Nets , 2006, Neural Computation.
[29] Yi Zhang,et al. Classifying Software Changes: Clean or Buggy? , 2008, IEEE Transactions on Software Engineering.
[30] David Lo,et al. Tag recommendation in software information sites , 2013, 2013 10th Working Conference on Mining Software Repositories (MSR).
[31] Geoffrey Zweig,et al. Recent advances in deep learning for speech research at Microsoft , 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.
[32] Jianping Yin,et al. A Stock Decision Support System Based on ELM , 2014 .
[33] Yang Feng,et al. Towards more accurate multi-label software behavior learning , 2014, 2014 Software Evolution Week - IEEE Conference on Software Maintenance, Reengineering, and Reverse Engineering (CSMR-WCRE).
[34] Geoffrey E. Hinton,et al. Reducing the Dimensionality of Data with Neural Networks , 2006, Science.
[35] Jianping Yin,et al. A Stock Decision Support System based on DBNs ⋆ , 2014 .
[36] Gerardo Canfora,et al. Multi-objective Cross-Project Defect Prediction , 2013, 2013 IEEE Sixth International Conference on Software Testing, Verification and Validation.
[37] Marc'Aurelio Ranzato,et al. Sparse Feature Learning for Deep Belief Networks , 2007, NIPS.
[38] Premkumar T. Devanbu,et al. Recalling the "imprecision" of cross-project defect prediction , 2012, SIGSOFT FSE.
[39] Jeffrey C. Carver,et al. Characterizing Software Architecture Changes: An Initial Study , 2007, ESEM 2007.
[40] Premkumar T. Devanbu,et al. How, and why, process metrics are better , 2013, 2013 35th International Conference on Software Engineering (ICSE).
[41] Harvey P. Siy,et al. Predicting Fault Incidence Using Software Change History , 2000, IEEE Trans. Software Eng..
[42] Audris Mockus,et al. Predicting risk of software changes , 2000, Bell Labs Technical Journal.