Benchmarking Library Recognition in Tweets
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[1] Lingming Zhang,et al. Deep just-in-time defect prediction: how far are we? , 2021, ISSTA.
[2] Mehrdad Sabetzadeh,et al. Using Domain-Specific Corpora for Improved Handling of Ambiguity in Requirements , 2021, 2021 IEEE/ACM 43rd International Conference on Software Engineering (ICSE).
[3] Qingkai Zeng,et al. Traceability Transformed: Generating More Accurate Links with Pre-Trained BERT Models , 2021, 2021 IEEE/ACM 43rd International Conference on Software Engineering (ICSE).
[4] Ming Zhou,et al. GraphCodeBERT: Pre-training Code Representations with Data Flow , 2020, ICLR.
[5] Ting Zhang,et al. Sentiment Analysis for Software Engineering: How Far Can Pre-trained Transformer Models Go? , 2020, 2020 IEEE International Conference on Software Maintenance and Evolution (ICSME).
[6] Hemank Lamba,et al. Need for Tweet: How Open Source Developers Talk About Their GitHub Work on Twitter , 2020, 2020 IEEE/ACM 17th International Conference on Mining Software Repositories (MSR).
[7] Liyan Song,et al. An Investigation of Cross-Project Learning in Online Just-In-Time Software Defect Prediction , 2020, 2020 IEEE/ACM 42nd International Conference on Software Engineering (ICSE).
[8] Dat Quoc Nguyen,et al. BERTweet: A pre-trained language model for English Tweets , 2020, EMNLP.
[9] Alan Ritter,et al. Code and Named Entity Recognition in StackOverflow , 2020, ACL.
[10] Xipeng Qiu,et al. Pre-trained models for natural language processing: A survey , 2020, Science China Technological Sciences.
[11] Ting Liu,et al. CodeBERT: A Pre-Trained Model for Programming and Natural Languages , 2020, FINDINGS.
[12] Aditya Kanade,et al. Learning and Evaluating Contextual Embedding of Source Code , 2019, ICML.
[13] Lysandre Debut,et al. HuggingFace's Transformers: State-of-the-art Natural Language Processing , 2019, ArXiv.
[14] Natalia Gimelshein,et al. PyTorch: An Imperative Style, High-Performance Deep Learning Library , 2019, NeurIPS.
[15] Omer Levy,et al. RoBERTa: A Robustly Optimized BERT Pretraining Approach , 2019, ArXiv.
[16] Yiming Yang,et al. XLNet: Generalized Autoregressive Pretraining for Language Understanding , 2019, NeurIPS.
[17] Christoph Treude,et al. SIEVE: Helping developers sift wheat from chaff via cross-platform analysis , 2018, Empirical Software Engineering.
[18] Frank Hutter,et al. Decoupled Weight Decay Regularization , 2017, ICLR.
[19] Ming-Wei Chang,et al. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding , 2019, NAACL.
[20] Bastin Tony Roy Savarimuthu,et al. Formal in the Informal: A Multi-Level Analysis of Core Python Developers' Tweets , 2018, 2018 25th Australasian Software Engineering Conference (ASWEC).
[21] Quoc V. Le,et al. A Simple Method for Commonsense Reasoning , 2018, ArXiv.
[22] Zhenchang Xing,et al. APIReal: an API recognition and linking approach for online developer forums , 2018, Empirical Software Engineering.
[23] Nicole Novielli,et al. Sentiment Polarity Detection for Software Development , 2017, Empirical Software Engineering.
[24] Mohamed Ibrahim,et al. A Little Bird Told Me: Mining Tweets for Requirements and Software Evolution , 2017, 2017 IEEE 25th International Requirements Engineering Conference (RE).
[25] Norbert Seyff,et al. An exploratory study of Twitter messages about software applications , 2017, Requirements Engineering.
[26] Baowen Xu,et al. An Improved SDA Based Defect Prediction Framework for Both Within-Project and Cross-Project Class-Imbalance Problems , 2017, IEEE Transactions on Software Engineering.
[27] Norbert Seyff,et al. A Needle in a Haystack: What Do Twitter Users Say about Software? , 2016, 2016 IEEE 24th International Requirements Engineering Conference (RE).
[28] Chanchal Kumar Roy,et al. RACK: Automatic API Recommendation Using Crowdsourced Knowledge , 2016, 2016 IEEE 23rd International Conference on Software Analysis, Evolution, and Reengineering (SANER).
[29] Sunghun Kim,et al. Crowd debugging , 2015, ESEC/SIGSOFT FSE.
[30] Sanja Fidler,et al. Aligning Books and Movies: Towards Story-Like Visual Explanations by Watching Movies and Reading Books , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).
[31] David Lo,et al. NIRMAL: Automatic identification of software relevant tweets leveraging language model , 2015, 2015 IEEE 22nd International Conference on Software Analysis, Evolution, and Reengineering (SANER).
[32] Jeffrey Pennington,et al. GloVe: Global Vectors for Word Representation , 2014, EMNLP.
[33] David Lo,et al. SEWordSim: software-specific word similarity database , 2014, ICSE Companion.
[34] Leif Singer,et al. Software engineering at the speed of light: how developers stay current using twitter , 2014, ICSE.
[35] David Lo,et al. Automated construction of a software-specific word similarity database , 2014, 2014 Software Evolution Week - IEEE Conference on Software Maintenance, Reengineering, and Reverse Engineering (CSMR-WCRE).
[36] Jeffrey Dean,et al. Distributed Representations of Words and Phrases and their Compositionality , 2013, NIPS.
[37] M. McHugh. Interrater reliability: the kappa statistic , 2012, Biochemia medica.
[38] David Lo,et al. Automatic classification of software related microblogs , 2012, 2012 28th IEEE International Conference on Software Maintenance (ICSM).
[39] David Lo,et al. Observatory of trends in software related microblogs , 2012, 2012 Proceedings of the 27th IEEE/ACM International Conference on Automated Software Engineering.
[40] Christoph Treude,et al. The impact of social media on software engineering practices and tools , 2010, FoSER '10.
[41] Yoshua Bengio,et al. Word Representations: A Simple and General Method for Semi-Supervised Learning , 2010, ACL.
[42] Roberto Navigli,et al. Word sense disambiguation: A survey , 2009, CSUR.
[43] Andrew McCallum,et al. Dynamic conditional random fields: factorized probabilistic models for labeling and segmenting sequence data , 2004, J. Mach. Learn. Res..
[44] George A. Miller,et al. WordNet: A Lexical Database for English , 1995, HLT.
[45] Robert L. Mercer,et al. Class-Based n-gram Models of Natural Language , 1992, CL.