• case-insensitive prefix search: default
  e.g., sig matches "SIGIR" as well as "signal"
• exact word search: append dollar sign ($) to word
  e.g., graph$ matches "graph", but not "graphics"
• boolean and: separate words by space
  e.g., codd model
• boolean or: connect words by pipe symbol (|)
  e.g., graph|network

Update May 7, 2017: Please note that we had to disable the phrase search operator (.) and the boolean not operator (-) due to technical problems. For the time being, phrase search queries will yield regular prefix search result, and search terms preceded by a minus will be interpreted as regular (positive) search terms.

found 54 matches

• 2023
• 
  Mikhail Evtikhiev, Egor Bogomolov, Yaroslav Sokolov, Timofey Bryksin:
  Out of the BLEU: How should we assess quality of the Code Generation models? J. Syst. Softw. 203: 111741 (2023)
• 
  Zhaoqiang Guo, Shiran Liu, Xutong Liu, Wei Lai, Mingliang Ma, Xu Zhang, Chao Ni, Yibiao Yang, Yanhui Li, Lin Chen, Guoqiang Zhou, Yuming Zhou:
  Code-line-level Bugginess Identification: How Far have We Come, and How Far have We Yet to Go? ACM Trans. Softw. Eng. Methodol. 32(4): 102:1-102:55 (2023)
• 
  Pengcheng Yin, Wen-Ding Li, Kefan Xiao, Abhishek Rao, Yeming Wen, Kensen Shi, Joshua Howland, Paige Bailey, Michele Catasta, Henryk Michalewski, Oleksandr Polozov, Charles Sutton:
  Natural Language to Code Generation in Interactive Data Science Notebooks. ACL (1) 2023: 126-173
• 
  Sam Lau, Philip J. Guo:
  From "Ban It Till We Understand It" to "Resistance is Futile": How University Programming Instructors Plan to Adapt as More Students Use AI Code Generation and Explanation Tools such as ChatGPT and GitHub Copilot. ICER (1) 2023: 106-121
• 
  Wengran Wang, Yudong Rao, Archit Kwatra, Alexandra Milliken, Yihuan Dong, Neeloy Gomes, Sarah Martin, Veronica Cateté, Amy Isvik, Tiffany Barnes, Chris Martens, Thomas W. Price:
  A Case Study on When and How Novices Use Code Examples in Open-Ended Programming. ITiCSE (1) 2023: 82-88
• 
  Shangwen Wang, Mingyang Geng, Bo Lin, Zhensu Sun, Ming Wen, Yepang Liu, Li Li, Tegawendé F. Bissyandé, Xiaoguang Mao:
  Natural Language to Code: How Far Are We? ESEC/SIGSOFT FSE 2023: 375-387
• 
  Xin Zhou, Kisub Kim, Bowen Xu, DongGyun Han, Junda He, David Lo:
  Generation-based Code Review Automation: How Far Are We? CoRR abs/2303.07221 (2023)
• 
  Weisong Sun, Chunrong Fang, Yudu You, Yun Miao, Yi Liu, Yuekang Li, Gelei Deng, Shenghan Huang, Yuchen Chen, Quanjun Zhang, Hanwei Qian, Yang Liu, Zhenyu Chen:
  Automatic Code Summarization via ChatGPT: How Far Are We? CoRR abs/2305.12865 (2023)
• 
  Kisub Kim, Xin Zhou, Dongsun Kim, Julia Lawall, Kui Liu, Tegawendé F. Bissyandé, Jacques Klein, Jaekwon Lee, David Lo:
  How are We Detecting Inconsistent Method Names? An Empirical Study from Code Review Perspective. CoRR abs/2308.12701 (2023)
• 
  Majeed Kazemitabaar, Xinying Hou, Austin Henley, Barbara J. Ericson, David Weintrop, Tovi Grossman:
  How Novices Use LLM-Based Code Generators to Solve CS1 Coding Tasks in a Self-Paced Learning Environment. CoRR abs/2309.14049 (2023)
• 2022
• 
  Bharathi Raja Chakravarthi, Adeep Hande, Rahul Ponnusamy, Prasanna Kumar Kumaresan, Ruba Priyadharshini:
  How can we detect Homophobia and Transphobia? Experiments in a multilingual code-mixed setting for social media governance. Int. J. Inf. Manag. Data Insights 2(2): 100119 (2022)
• 
  Tomasz Lewowski, Lech Madeyski:
  How far are we from reproducible research on code smell detection? A systematic literature review. Inf. Softw. Technol. 144: 106783 (2022)
• 
  Ruiyin Wen, Maxime Lamothe, Shane McIntosh:
  How Does Code Reviewing Feedback Evolve?: A Longitudinal Study at Dell EMC. ICSE (SEIP) 2022: 151-160
• 
  Lawrence Chen, Peter C. Rigby, Nachiappan Nagappan:
  Understanding why we cannot model how long a code review will take: an industrial case study. ESEC/SIGSOFT FSE 2022: 1314-1319
• 
  Jaydeb Sarker, Asif Kamal Turzo, Ming Dong, Amiangshu Bosu:
  Automated Identification of Toxic Code Reviews: How Far Can We Go? CoRR abs/2202.13056 (2022)
• 
  Mikhail Evtikhiev, Egor Bogomolov, Yaroslav Sokolov, Timofey Bryksin:
  Out of the BLEU: how should we assess quality of the Code Generation models? CoRR abs/2208.03133 (2022)
• 
  Guang Yang, Yu Zhou, Wenhua Yang, Tao Yue, Xiang Chen, Taolue Chen:
  How Important are Good Method Names in Neural Code Generation? A Model Robustness Perspective. CoRR abs/2211.15844 (2022)
• 
  Pengcheng Yin, Wen-Ding Li, Kefan Xiao, Abhishek Rao, Yeming Wen, Kensen Shi, Joshua Howland, Paige Bailey, Michele Catasta, Henryk Michalewski, Alex Polozov, Charles Sutton:
  Natural Language to Code Generation in Interactive Data Science Notebooks. CoRR abs/2212.09248 (2022)
• 2021
• 
  Klaus Rechert, Jurek Oberhauser, Rafael Gieschke:
  How Long Can We Build It? Ensuring Usability of a Scientific Code Base. Int. J. Digit. Curation 16(1): 11 (2021)
• 
  Dana-Maria Iliescu, Rasmus Grand, Sara Qirko, Rob van der Goot:
  Much Gracias: Semi-supervised Code-switch Detection for Spanish-English: How far can we get? CALCS@NAACL 2021: 65-71
• 
  Michael J. Muller, April Yi Wang, Steven I. Ross, Justin D. Weisz, Mayank Agarwal, Kartik Talamadupula, Stephanie Houde, Fernando Martinez, John T. Richards, Jaimie Drozdal, Xuye Liu, David Piorkowski, Dakuo Wang:
  How Data Scientists Improve Generated Code Documentation in Jupyter Notebooks. IUI Workshops 2021
• 
  Thomas Hirsch, Birgit Hofer:
  What we can learn from how programmers debug their code. SER&IP@ICSE 2021: 37-40
• 
  Thomas Hirsch, Birgit Hofer:
  What we can learn from how programmers debug their code. CoRR abs/2103.12447 (2021)
• 
  Ensheng Shi, Yanlin Wang, Lun Du, Junjie Chen, Shi Han, Hongyu Zhang, Dongmei Zhang, Hongbin Sun:
  Neural Code Summarization: How Far Are We? CoRR abs/2107.07112 (2021)
• 2020
• 
  David Williamson Shaffer, Andrew R. Ruis:
  How We Code. ICQE 2020: 62-77
• 
  Bodin Chinthanet, Serena Elisa Ponta, Henrik Plate, Antonino Sabetta, Raula Gaikovina Kula, Takashi Ishio, Kenichi Matsumoto:
  Code-Based Vulnerability Detection in Node.js Applications: How far are we? ASE 2020: 1199-1203
• 
  Bodin Chinthanet, Serena Elisa Ponta, Henrik Plate, Antonino Sabetta, Raula Gaikovina Kula, Takashi Ishio, Kenichi Matsumoto:
  Code-based Vulnerability Detection in Node.js Applications: How far are we? CoRR abs/2008.04568 (2020)
• 2019
• 
  Weiqin Zou, Jifeng Xuan, Xiaoyuan Xie, Zhenyu Chen, Baowen Xu:
  How does code style inconsistency affect pull request integration? An exploratory study on 117 GitHub projects. Empir. Softw. Eng. 24(6): 3871-3903 (2019)
• 
  Weijia Wang, Yu Yu, François-Xavier Standaert:
  Provable Order Amplification for Code-Based Masking: How to Avoid Non-Linear Leakages Due to Masked Operations. IEEE Trans. Inf. Forensics Secur. 14(11): 3069-3082 (2019)
• 
  Yu Liang, Wenjun Wu, Lisha Wu, Meng Wang:
  Inferring How Novice Students Learn to Code: Integrating Automated Program Repair with Cognitive Model. Big Data (CCF) 2019: 46-56

skipping 24 more matches