Natural Language Processing and Program Analysis for Supporting Todo Comments as Software Evolves.
暂无分享,去创建一个
[1] Raymond J. Mooney,et al. Language to Code: Learning Semantic Parsers for If-This-Then-That Recipes , 2015, ACL.
[2] Junyi Jessy Li,et al. Fast and Accurate Prediction of Sentence Specificity , 2015, AAAI.
[3] Premkumar T. Devanbu,et al. On the naturalness of software , 2016, Commun. ACM.
[4] James R. Cordy,et al. TXL - A Language for Programming Language Tools and Applications , 2004, LDTA@ETAPS.
[5] Yuanyuan Zhou,et al. Have things changed now?: an empirical study of bug characteristics in modern open source software , 2006, ASID '06.
[6] Yuanyuan Zhou,et al. /*icomment: bugs or bad comments?*/ , 2007, SOSP.
[7] Rikard Andersson,et al. Mining Relations from Git Commit Messages : an Experience Report , 2014 .
[8] Giriprasad Sridhara. Automatically Detecting the Up-To-Date Status of ToDo Comments in Java Programs , 2016, ISEC.
[9] Andreas Krause,et al. Predicting Program Properties from "Big Code" , 2015, POPL.
[10] Premkumar T. Devanbu,et al. Recovering clear, natural identifiers from obfuscated JS names , 2017, ESEC/SIGSOFT FSE.
[11] Janice Singer,et al. TODO or to bug , 2008, 2008 ACM/IEEE 30th International Conference on Software Engineering.
[12] Raymond J. Mooney,et al. Dialog for Language to Code , 2017, IJCNLP.
[13] William W. Cohen,et al. Natural Language Models for Predicting Programming Comments , 2013, ACL.
[14] Tomoki Toda,et al. Learning to Generate Pseudo-Code from Source Code Using Statistical Machine Translation (T) , 2015, 2015 30th IEEE/ACM International Conference on Automated Software Engineering (ASE).
[15] David Lo,et al. Duplicate bug report detection with a combination of information retrieval and topic modeling , 2012, 2012 Proceedings of the 27th IEEE/ACM International Conference on Automated Software Engineering.
[16] Armando Solar-Lezama,et al. Program synthesis from polymorphic refinement types , 2015, PLDI.
[17] Mario Linares Vásquez,et al. ChangeScribe: A Tool for Automatically Generating Commit Messages , 2015, 2015 IEEE/ACM 37th IEEE International Conference on Software Engineering.
[18] Yutaka Matsuo,et al. A Neural Architecture for Generating Natural Language Descriptions from Source Code Changes , 2017, ACL.
[19] Michael D. Ernst. Natural Language is a Programming Language: Applying Natural Language Processing to Software Development , 2017, SNAPL.
[20] Harald C. Gall,et al. Do Code and Comments Co-Evolve? On the Relation between Source Code and Comment Changes , 2007, 14th Working Conference on Reverse Engineering (WCRE 2007).
[21] Houari A. Sahraoui,et al. How Good is Your Comment? A Study of Comments in Java Programs , 2011, 2011 International Symposium on Empirical Software Engineering and Measurement.
[22] Collin McMillan,et al. Towards Automatic Generation of Short Summaries of Commits , 2017, 2017 IEEE/ACM 25th International Conference on Program Comprehension (ICPC).
[23] Michael D. Ernst,et al. Feedback-Directed Random Test Generation , 2007, 29th International Conference on Software Engineering (ICSE'07).
[24] Giuliano Antoniol,et al. The Use of Text Retrieval and Natural Language Processing in Software Engineering , 2015, 2015 IEEE/ACM 37th IEEE International Conference on Software Engineering.
[25] Ani Nenkova,et al. Easily Identifiable Discourse Relations , 2008, COLING.
[26] Yuanyuan Zhou,et al. Listening to programmers — Taxonomies and characteristics of comments in operating system code , 2009, 2009 IEEE 31st International Conference on Software Engineering.
[27] Tao Xie,et al. Inferring specifications for resources from natural language API documentation , 2011, Automated Software Engineering.
[28] Charles A. Sutton,et al. A Convolutional Attention Network for Extreme Summarization of Source Code , 2016, ICML.