Automatically recommending components for issue reports using deep learning
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Aditya K. Ghose | Morakot Choetkiertikul | Truyen Tran | Khanh Hoa Dam | Chaiyong Ragkhitwetsagul | Trang Pham | K. Dam | T. Tran | A. Ghose | Chaiyong Ragkhitwetsagul | Trang Pham | Morakot Choetkiertikul
[1] Feng Xu,et al. Bug Triaging Based on Tossing Sequence Modeling , 2019, Journal of Computer Science and Technology.
[2] Madhu Kumari,et al. An Improved Classifier Based on Entropy and Deep Learning for Bug Priority Prediction , 2018, ISDA.
[3] Feng Xu,et al. An Effective Approach for Routing the Bug Reports to the Right Fixers , 2018, Internetware.
[4] Cristina V. Lopes,et al. Oreo: detection of clones in the twilight zone , 2018, ESEC/SIGSOFT FSE.
[5] Fahim Mohammad,et al. Is preprocessing of text really worth your time for online comment classification? , 2018, ArXiv.
[6] Aditya K. Ghose,et al. Poster: Predicting Components for Issue Reports Using Deep Learning with Information Retrieval , 2018, 2018 IEEE/ACM 40th International Conference on Software Engineering: Companion (ICSE-Companion).
[7] Senthil Mani,et al. DeepTriage: Exploring the Effectiveness of Deep Learning for Bug Triaging , 2018, COMAD/CODS.
[8] Debarshi Kumar Sanyal,et al. Automated classification of software issue reports using machine learning techniques: an empirical study , 2017, Innovations in Systems and Software Engineering.
[9] Guillermo Licea,et al. Towards Supporting Software Engineering Using Deep Learning: A Case of Software Requirements Classification , 2017, 2017 5th International Conference in Software Engineering Research and Innovation (CONISOFT).
[10] Barbara G. Ryder,et al. CCLearner: A Deep Learning-Based Clone Detection Approach , 2017, 2017 IEEE International Conference on Software Maintenance and Evolution (ICSME).
[11] Chan-Gun Lee,et al. Applying deep learning based automatic bug triager to industrial projects , 2017, ESEC/SIGSOFT FSE.
[12] Ruchika Malhotra,et al. Prediction of defect severity by mining software project reports , 2017, Int. J. Syst. Assur. Eng. Manag..
[13] Anh Tuan Nguyen,et al. Bug Localization with Combination of Deep Learning and Information Retrieval , 2017, 2017 IEEE/ACM 25th International Conference on Program Comprehension (ICPC).
[14] Tobias Glasmachers,et al. Limits of End-to-End Learning , 2017, ACML.
[15] David Lo,et al. Improving Automated Bug Triaging with Specialized Topic Model , 2017, IEEE Transactions on Software Engineering.
[16] Tim Menzies,et al. Easy over hard: a case study on deep learning , 2017, ESEC/SIGSOFT FSE.
[17] Wenpeng Yin,et al. Comparative Study of CNN and RNN for Natural Language Processing , 2017, ArXiv.
[18] Jin Liu,et al. Scalable tag recommendation for software information sites , 2017, 2017 IEEE 24th International Conference on Software Analysis, Evolution and Reengineering (SANER).
[19] Martin White,et al. Deep learning code fragments for code clone detection , 2016, 2016 31st IEEE/ACM International Conference on Automated Software Engineering (ASE).
[20] Truyen Tran,et al. A deep language model for software code , 2016, FSE 2016.
[21] Xiaodong Gu,et al. Deep API learning , 2016, SIGSOFT FSE.
[22] Song Wang,et al. Automatically Learning Semantic Features for Defect Prediction , 2016, 2016 IEEE/ACM 38th International Conference on Software Engineering (ICSE).
[23] Mark Harman,et al. Multi-objective Software Effort Estimation , 2016, 2016 IEEE/ACM 38th International Conference on Software Engineering (ICSE).
[24] John Salvatier,et al. Theano: A Python framework for fast computation of mathematical expressions , 2016, ArXiv.
[25] Dan Yang,et al. A component recommender for bug reports using Discriminative Probability Latent Semantic Analysis , 2016, Inf. Softw. Technol..
[26] Ahmed Fawzi Otoom,et al. Severity prediction of software bugs , 2016, 2016 7th International Conference on Information and Communication Systems (ICICS).
[27] Andrea Janes,et al. What recommendation systems for software engineering recommend: A systematic literature review , 2016, J. Syst. Softw..
[28] Anh Tuan Nguyen,et al. Combining Deep Learning with Information Retrieval to Localize Buggy Files for Bug Reports (N) , 2015, 2015 30th IEEE/ACM International Conference on Automated Software Engineering (ASE).
[29] Mario Linares Vásquez,et al. Automated Tagging of Software Projects Using Bytecode and Dependencies (N) , 2015, 2015 30th IEEE/ACM International Conference on Automated Software Engineering (ASE).
[30] Xinli Yang,et al. Deep Learning for Just-in-Time Defect Prediction , 2015, 2015 IEEE International Conference on Software Quality, Reliability and Security.
[31] Martin White,et al. Toward Deep Learning Software Repositories , 2015, 2015 IEEE/ACM 12th Working Conference on Mining Software Repositories.
[32] Matthew J. Hausknecht,et al. Beyond short snippets: Deep networks for video classification , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[33] David Lo,et al. Dual analysis for recommending developers to resolve bugs , 2015, J. Softw. Evol. Process..
[34] Christoph Trattner,et al. Mining, Modeling, and Recommending 'Things' in Social Media , 2014, Lecture Notes in Computer Science.
[35] Tong Zhang,et al. Effective Use of Word Order for Text Categorization with Convolutional Neural Networks , 2014, NAACL.
[36] Hao Hu,et al. Effective Bug Triage Based on Historical Bug-Fix Information , 2014, 2014 IEEE 25th International Symposium on Software Reliability Engineering.
[37] Uirá Kulesza,et al. An Empirical Study of Delays in the Integration of Addressed Issues , 2014, 2014 IEEE International Conference on Software Maintenance and Evolution.
[38] David Lo,et al. EnTagRec++: An enhanced tag recommendation system for software information sites , 2014, 2014 IEEE International Conference on Software Maintenance and Evolution.
[39] David Lo,et al. Compositional Vector Space Models for Improved Bug Localization , 2014, 2014 IEEE International Conference on Software Maintenance and Evolution.
[40] David Lo,et al. Automatic Fine-Grained Issue Report Reclassification , 2014, 2014 19th International Conference on Engineering of Complex Computer Systems.
[41] David Lo,et al. Automated prediction of bug report priority using multi-factor analysis , 2014, Empirical Software Engineering.
[42] Georgiana Dinu,et al. Don’t count, predict! A systematic comparison of context-counting vs. context-predicting semantic vectors , 2014, ACL.
[43] Collin McMillan,et al. On using machine learning to automatically classify software applications into domain categories , 2014, Empirical Software Engineering.
[44] Quoc V. Le,et al. Distributed Representations of Sentences and Documents , 2014, ICML.
[45] Jürgen Schmidhuber,et al. Deep learning in neural networks: An overview , 2014, Neural Networks.
[46] Ming Wen,et al. An empirical study of bug report field reassignment , 2014, 2014 Software Evolution Week - IEEE Conference on Software Maintenance, Reengineering, and Reverse Engineering (CSMR-WCRE).
[47] Aftab Iqbal,et al. Understanding Contributor to Developer Turnover Patterns in OSS Projects: A Case Study of Apache Projects , 2014 .
[48] Gang Yin,et al. Tag recommendation for open source software , 2013, Frontiers of Computer Science.
[49] Dominik Kowald,et al. Refining Frequency-Based Tag Reuse Predictions by Means of Time and Semantic Context , 2013, MSM/MUSE.
[50] Dewayne E. Perry,et al. Toward understanding the causes of unanswered questions in software information sites: a case study of stack overflow , 2013, ESEC/FSE 2013.
[51] Kenneth H. Rose. A Guide to the Project Management Body of Knowledge (PMBOK® Guide)—Fifth Edition , 2013 .
[52] David Lo,et al. Tag recommendation in software information sites , 2013, 2013 10th Working Conference on Mining Software Repositories (MSR).
[53] Geoffrey E. Hinton,et al. Speech recognition with deep recurrent neural networks , 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.
[54] Serge Demeyer,et al. Predicting Reassignments of Bug Reports - An Exploratory Investigation , 2013, 2013 17th European Conference on Software Maintenance and Reengineering.
[55] Ferdian Thung,et al. Automatic Defect Categorization , 2012, 2012 19th Working Conference on Reverse Engineering.
[56] Ioannis Stamelos,et al. Extracting Components from Open Source: The Component Adaptation Environment (COPE) Approach , 2012, 2012 38th Euromicro Conference on Software Engineering and Advanced Applications.
[57] Oliver Denninger,et al. Recommending relevant code artifacts for change requests using multiple predictors , 2012, 2012 Third International Workshop on Recommendation Systems for Software Engineering (RSSE).
[58] Jian Zhou,et al. Where should the bugs be fixed? More accurate information retrieval-based bug localization based on bug reports , 2012, 2012 34th International Conference on Software Engineering (ICSE).
[59] Ashish Sureka,et al. Learning to Classify Bug Reports into Components , 2012, TOOLS.
[60] Junjie Yao,et al. Challenging the Long Tail Recommendation , 2012, Proc. VLDB Endow..
[61] Gail C. Murphy,et al. Automatic categorization of bug reports using latent Dirichlet allocation , 2012, ISEC.
[62] Hung Viet Nguyen,et al. A topic-based approach for narrowing the search space of buggy files from a bug report , 2011, 2011 26th IEEE/ACM International Conference on Automated Software Engineering (ASE 2011).
[63] Siau-Cheng Khoo,et al. Towards more accurate retrieval of duplicate bug reports , 2011, 2011 26th IEEE/ACM International Conference on Automated Software Engineering (ASE 2011).
[64] Gail C. Murphy,et al. Reducing the effort of bug report triage: Recommenders for development-oriented decisions , 2011, TSEM.
[65] Lionel C. Briand,et al. A practical guide for using statistical tests to assess randomized algorithms in software engineering , 2011, 2011 33rd International Conference on Software Engineering (ICSE).
[66] Everton Alvares Cherman,et al. Multi-label Problem Transformation Methods: a Case Study , 2011, CLEI Electron. J..
[67] Serge Demeyer,et al. Comparing Mining Algorithms for Predicting the Severity of a Reported Bug , 2011, 2011 15th European Conference on Software Maintenance and Reengineering.
[68] Ahmed Tamrawi,et al. Fuzzy set approach for automatic tagging in evolving software , 2010, 2010 IEEE International Conference on Software Maintenance.
[69] Harald Steck,et al. Training and testing of recommender systems on data missing not at random , 2010, KDD.
[70] Martin P. Robillard,et al. Recommendation Systems for Software Engineering , 2010, IEEE Software.
[71] Bart Goethals,et al. Predicting the severity of a reported bug , 2010, 2010 7th IEEE Working Conference on Mining Software Repositories (MSR 2010).
[72] Thomas Zimmermann,et al. Optimized assignment of developers for fixing bugs an initial evaluation for eclipse projects , 2009, 2009 3rd International Symposium on Empirical Software Engineering and Measurement.
[73] Robert J. Walker,et al. Semi-automating small-scale source code reuse via structural correspondence , 2008, SIGSOFT '08/FSE-16.
[74] Foutse Khomh,et al. Is it a bug or an enhancement?: a text-based approach to classify change requests , 2008, CASCON '08.
[75] Alexander Tuzhilin,et al. The long tail of recommender systems and how to leverage it , 2008, RecSys '08.
[76] Tim Menzies,et al. Automated severity assessment of software defect reports , 2008, 2008 IEEE International Conference on Software Maintenance.
[77] Nicholas Jalbert,et al. Automated duplicate detection for bug tracking systems , 2008, 2008 IEEE International Conference on Dependable Systems and Networks With FTCS and DCC (DSN).
[78] Tao Xie,et al. An approach to detecting duplicate bug reports using natural language and execution information , 2008, 2008 ACM/IEEE 30th International Conference on Software Engineering.
[79] Per Runeson,et al. Detection of Duplicate Defect Reports Using Natural Language Processing , 2007, 29th International Conference on Software Engineering (ICSE'07).
[80] Gail C. Murphy,et al. Who should fix this bug? , 2006, ICSE.
[81] Michael I. Jordan,et al. Latent Dirichlet Allocation , 2001, J. Mach. Learn. Res..
[82] Jason Weston,et al. A kernel method for multi-labelled classification , 2001, NIPS.
[83] Jürgen Schmidhuber,et al. Learning to Forget: Continual Prediction with LSTM , 2000, Neural Computation.
[84] A. Vargha,et al. A Critique and Improvement of the CL Common Language Effect Size Statistics of McGraw and Wong , 2000 .
[85] S. Hochreiter,et al. Long Short-Term Memory , 1997, Neural Computation.
[86] Huaiyu Zhu. On Information and Sufficiency , 1997 .
[87] Albert L. Lederer,et al. Nine management guidelines for better cost estimating , 1992, CACM.
[88] Jacob Cohen. Statistical Power Analysis for the Behavioral Sciences , 1969, The SAGE Encyclopedia of Research Design.
[89] R. A. Leibler,et al. On Information and Sufficiency , 1951 .
[90] E. R. James. Some Implications of Remedial and Preventive Legislation in the United States , 1913, American Journal of Sociology.
[91] Nitish Srivastava,et al. Dropout: a simple way to prevent neural networks from overfitting , 2014, J. Mach. Learn. Res..
[92] Aapo Hyvärinen,et al. Noise-Contrastive Estimation of Unnormalized Statistical Models, with Applications to Natural Image Statistics , 2012, J. Mach. Learn. Res..
[93] Hermann Ney,et al. LSTM Neural Networks for Language Modeling , 2012, INTERSPEECH.
[94] M. Rahman,et al. Optimized Assignment of Developers for Fixing Bugs , 2009 .
[95] Min-Ling Zhang,et al. Multi-Label Neural Networks with Applications to Functional Genomics and Text Categorization , 2006, IEEE Trans. Knowl. Data Eng..
[96] Capers Jones,et al. Software Project Management Practices: Failure Versus Success © , 2004 .
[97] Gail C. Murphy,et al. Automatic bug triage using text categorization , 2004, SEKE.
[98] Teresa Foo,et al. : SYSTEMATIC LITERATURE REVIEW , 2004 .
[99] Yoshua Bengio,et al. Gradient Flow in Recurrent Nets: the Difficulty of Learning Long-Term Dependencies , 2001 .
[100] Harold Kerzner,et al. Project management workbook to accompany project management : a systems approach to planning, scheduling and controlling , 2001 .
[101] J. T. Lochner. The Journal of Defense Software Engineering , 1999 .
[102] Andrew McCallum,et al. A comparison of event models for naive bayes text classification , 1998, AAAI 1998.