Effective API Recommendation without Historical Software Repositories
暂无分享,去创建一个
[1] Rosco Hill,et al. Automatic method completion , 2004, Proceedings. 19th International Conference on Automated Software Engineering, 2004..
[2] Romain Robbes,et al. How Program History Can Improve Code Completion , 2008, 2008 23rd IEEE/ACM International Conference on Automated Software Engineering.
[3] Chanchal Kumar Roy,et al. Exploring API method parameter recommendations , 2015, 2015 IEEE International Conference on Software Maintenance and Evolution (ICSME).
[4] Hong Cheng,et al. Searching connected API subgraph via text phrases , 2012, SIGSOFT FSE.
[5] Premkumar T. Devanbu,et al. Recommending random walks , 2007, ESEC-FSE '07.
[6] Chanchal Kumar Roy,et al. A Simple, Efficient, Context‐sensitive Approach for Code Completion , 2016, J. Softw. Evol. Process..
[7] Dongmei Zhang,et al. CodeHow: Effective Code Search Based on API Understanding and Extended Boolean Model (E) , 2015, 2015 30th IEEE/ACM International Conference on Automated Software Engineering (ASE).
[8] Martin White,et al. Toward Deep Learning Software Repositories , 2015, 2015 IEEE/ACM 12th Working Conference on Mining Software Repositories.
[9] Yi Zhang,et al. Automatic parameter recommendation for practical API usage , 2012, 2012 34th International Conference on Software Engineering (ICSE).
[10] Charles A. Sutton,et al. Learning natural coding conventions , 2014, SIGSOFT FSE.
[11] David Lo,et al. Automatic recommendation of API methods from feature requests , 2013, 2013 28th IEEE/ACM International Conference on Automated Software Engineering (ASE).
[12] Andrew D. Gordon,et al. Bimodal Modelling of Source Code and Natural Language , 2015, ICML.
[13] Steven P. Reiss,et al. Semantics-based code search , 2009, 2009 IEEE 31st International Conference on Software Engineering.
[14] N. Cliff. Dominance statistics: Ordinal analyses to answer ordinal questions. , 1993 .
[15] Collin McMillan,et al. ExPort: Detecting and visualizing API usages in large source code repositories , 2013, 2013 28th IEEE/ACM International Conference on Automated Software Engineering (ASE).
[16] Tao Wang,et al. Convolutional Neural Networks over Tree Structures for Programming Language Processing , 2014, AAAI.
[17] Charles A. Sutton,et al. Parameter-Free Probabilistic API Mining at GitHub Scale , 2015, ArXiv.
[18] Xiaodong Gu,et al. Deep API learning , 2016, SIGSOFT FSE.
[19] J. H. FREMLIN,et al. “Significant” and “Highly Significant” , 1966, Nature.
[20] Eran Yahav,et al. Code completion with statistical language models , 2014, PLDI.
[21] Collin McMillan,et al. Recommending source code examples via API call usages and documentation , 2010, RSSE '10.
[22] Thomas D. LaToza,et al. Active code completion , 2011, 2012 34th International Conference on Software Engineering (ICSE).
[23] Kai Chen,et al. Mining succinct and high-coverage API usage patterns from source code , 2013, 2013 10th Working Conference on Mining Software Repositories (MSR).
[24] Collin McMillan,et al. Portfolio: finding relevant functions and their usage , 2011, 2011 33rd International Conference on Software Engineering (ICSE).
[25] Jian Pei,et al. MAPO: mining API usages from open source repositories , 2006, MSR '06.
[26] Daqing Hou,et al. An evaluation of the strategies of sorting, filtering, and grouping API methods for Code Completion , 2011, 2011 27th IEEE International Conference on Software Maintenance (ICSM).
[27] Bin Li,et al. MULAPI: Improving API method recommendation with API usage location , 2018, J. Syst. Softw..
[28] Mira Mezini,et al. Learning from examples to improve code completion systems , 2009, ESEC/SIGSOFT FSE.
[29] Ellen M. Voorhees,et al. The TREC-8 Question Answering Track Report , 1999, TREC.
[30] Kathryn T. Stolee,et al. Toward semantic search via SMT solver , 2012, SIGSOFT FSE.
[31] Thorsten Joachims,et al. Training linear SVMs in linear time , 2006, KDD '06.
[32] Trong Duc Nguyen,et al. Exploring API Embedding for API Usages and Applications , 2017, 2017 IEEE/ACM 39th International Conference on Software Engineering (ICSE).
[33] Danny Dig,et al. API code recommendation using statistical learning from fine-grained changes , 2016, SIGSOFT FSE.
[34] Daniel Tarlow,et al. Structured Generative Models of Natural Source Code , 2014, ICML.
[35] Martin P. Robillard,et al. Using Structure-Based Recommendations to Facilitate Discoverability in APIs , 2011, ECOOP.
[36] Charles A. Sutton,et al. Parameter-free probabilistic API mining across GitHub , 2015, SIGSOFT FSE.
[37] R. Holmes,et al. Using structural context to recommend source code examples , 2005, Proceedings. 27th International Conference on Software Engineering, 2005. ICSE 2005..
[38] Anh Tuan Nguyen,et al. Graph-Based Statistical Language Model for Code , 2015, 2015 IEEE/ACM 37th IEEE International Conference on Software Engineering.
[39] Chih-Jen Lin,et al. LIBLINEAR: A Library for Large Linear Classification , 2008, J. Mach. Learn. Res..