Improving bug detection via context-based code representation learning and attention-based neural networks
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Shaohua Wang | Tien N. Nguyen | Yi Li | Son Van Nguyen | T. Nguyen | Shaohua Wang | Yi Li | S. Nguyen
[1] Andreas Zeller,et al. Learning from 6,000 projects: lightweight cross-project anomaly detection , 2010, ISSTA '10.
[2] Bin Liang,et al. NAR-miner: discovering negative association rules from code for bug detection , 2018, ESEC/SIGSOFT FSE.
[3] Shan Lu,et al. Understanding and detecting real-world performance bugs , 2012, PLDI.
[4] Audris Mockus,et al. Identifying reasons for software changes using historic databases , 2000, Proceedings 2000 International Conference on Software Maintenance.
[5] Zhenmin Li,et al. PR-Miner: automatically extracting implicit programming rules and detecting violations in large software code , 2005, ESEC/FSE-13.
[6] Benjamin Livshits,et al. DynaMine: finding common error patterns by mining software revision histories , 2005, ESEC/FSE-13.
[7] J. David Morgenthaler,et al. Evaluating static analysis defect warnings on production software , 2007, PASTE '07.
[8] Geoffrey E. Hinton,et al. ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.
[9] David Hovemeyer,et al. Improving your software using static analysis to find bugs , 2006, OOPSLA '06.
[10] Charles A. Sutton,et al. A Convolutional Attention Network for Extreme Summarization of Source Code , 2016, ICML.
[11] Jure Leskovec,et al. node2vec: Scalable Feature Learning for Networks , 2016, KDD.
[12] Premkumar T. Devanbu,et al. A large scale study of programming languages and code quality in github , 2014, SIGSOFT FSE.
[13] Uri Alon,et al. code2vec: learning distributed representations of code , 2018, Proc. ACM Program. Lang..
[14] Isil Dillig,et al. Static detection of asymptotic performance bugs in collection traversals , 2015, PLDI.
[15] Martin White,et al. Deep learning code fragments for code clone detection , 2016, 2016 31st IEEE/ACM International Conference on Automated Software Engineering (ASE).
[16] Gang Zhao,et al. DeepSim: deep learning code functional similarity , 2018, ESEC/SIGSOFT FSE.
[17] Geoffrey E. Hinton,et al. GEMINI: Gradient Estimation Through Matrix Inversion After Noise Injection , 1988, NIPS.
[18] Joe D. Warren,et al. The program dependence graph and its use in optimization , 1987, TOPL.
[19] Martin T. Vechev,et al. PHOG: Probabilistic Model for Code , 2016, ICML.
[20] Lukasz Kaiser,et al. Attention is All you Need , 2017, NIPS.
[21] Jeffrey Dean,et al. Distributed Representations of Words and Phrases and their Compositionality , 2013, NIPS.
[22] Koushik Sen,et al. DeepBugs: a learning approach to name-based bug detection , 2018, Proc. ACM Program. Lang..
[23] Gary A. Kildall,et al. A unified approach to global program optimization , 1973, POPL.
[24] Premkumar T. Devanbu,et al. On the naturalness of software , 2016, Commun. ACM.
[25] Andreas Zeller,et al. Detecting object usage anomalies , 2007, ESEC-FSE '07.
[26] David Hovemeyer,et al. Finding more null pointer bugs, but not too many , 2007, PASTE '07.
[27] Hoan Anh Nguyen,et al. Graph-based mining of multiple object usage patterns , 2009, ESEC/FSE '09.
[28] Shuvendu K. Lahiri,et al. Code vectors: understanding programs through embedded abstracted symbolic traces , 2018, ESEC/SIGSOFT FSE.
[29] Tim Menzies,et al. Heterogeneous Defect Prediction , 2015, IEEE Transactions on Software Engineering.
[30] Devin Chollak,et al. Bugram: Bug detection with n-gram language models , 2016, 2016 31st IEEE/ACM International Conference on Automated Software Engineering (ASE).
[31] Yan Zhang,et al. AntMiner: Mining More Bugs by Reducing Noise Interference , 2016, 2016 IEEE/ACM 38th International Conference on Software Engineering (ICSE).
[32] Song Wang,et al. Automatically Learning Semantic Features for Defect Prediction , 2016, 2016 IEEE/ACM 38th International Conference on Software Engineering (ICSE).
[33] Tao Wang,et al. TBCNN: A Tree-Based Convolutional Neural Network for Programming Language Processing , 2014, ArXiv.
[34] Edward Yourdon. Structured programming and structured design as art forms , 1975, AFIPS '75.
[35] Rishabh Singh,et al. Automated Correction for Syntax Errors in Programming Assignments using Recurrent Neural Networks , 2016, ArXiv.
[36] Bowen Zhou,et al. ABCNN: Attention-Based Convolutional Neural Network for Modeling Sentence Pairs , 2015, TACL.
[37] Premkumar T. Devanbu,et al. On the "naturalness" of buggy code , 2015, ICSE.
[38] Sreeram Kannan,et al. Deepcode: Feedback Codes via Deep Learning , 2018, IEEE Journal on Selected Areas in Information Theory.
[39] Dawson R. Engler,et al. Bugs as deviant behavior: a general approach to inferring errors in systems code , 2001, SOSP.
[40] Michael Pradel,et al. Learning to Fuzz: Application-Independent Fuzz Testing with Probabilistic, Generative Models of Input Data , 2016 .
[41] Christopher D. Manning,et al. Improved Semantic Representations From Tree-Structured Long Short-Term Memory Networks , 2015, ACL.
[42] Yoshua Bengio,et al. Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation , 2014, EMNLP.
[43] Hoan Anh Nguyen,et al. Accurate and Efficient Structural Characteristic Feature Extraction for Clone Detection , 2009, FASE.
[44] Dan Grossman,et al. Taming the Static Analysis Beast , 2017, SNAPL.
[45] Gabriele Bavota,et al. Deep Learning Similarities from Different Representations of Source Code , 2018, 2018 IEEE/ACM 15th International Conference on Mining Software Repositories (MSR).
[46] Swarat Chaudhuri,et al. Neural Attribute Machines for Program Generation , 2017, ArXiv.
[47] Barbara G. Ryder,et al. CCLearner: A Deep Learning-Based Clone Detection Approach , 2017, 2017 IEEE International Conference on Software Maintenance and Evolution (ICSME).