Fetching the paper…
Reading the bibliography…
Modern language models (LMs) have been successfully employed in source code generation and understanding, leading to a significant increase in research focused on learning-based code intelligence, such as automated bug repair, and test case generation.
Two sides of the same coin: Exploiting the impact of identifiers in neural code comprehension. In 2023 IEEE/ACM 45th International Conference on Software Engineering (ICSE) . IEEE, 1933–1945
Shuzheng Gao, Cuiyun Gao, Chaozheng Wang, Jun Sun, David Lo, and Yue Yu. 2023a · 1945
Earlier work this paper cites.
Guidelines for performing Systematic Literature Reviews in Software Engineering
Barbara Kitchenham and Stuart Charters. 2007a · 2007
Earlier work this paper cites.
Guidelines for performing systematic literature reviews in software engineering
Barbara Kitchenham and Stuart Charters. 2007b · 2007
Earlier work this paper cites.
Benchmarking cloud serving systems with YCSB. In Proceedings of the 1st ACM symposium on Cloud computing . 143–154
Brian F Cooper, Adam Silberstein, Erwin Tam, Raghu Ramakrishnan, and Russell Sears. 2010 · 2010
Earlier work this paper cites.
Identifying relevant studies in software engineering
He Zhang, Muhammad Ali Babar, and Paolo Tell. 2011 · 2011
Earlier work this paper cites.
The seven deadly sins of cloud computing research. In 4th USENIX Workshop on Hot Topics in Cloud Computing (HotCloud 12)
Malte Schwarzkopf, Derek G Murray, and Steven Hand. 2012 · 2012
Earlier work this paper cites.
It’s not a bug, it’s a feature: how misclassification impacts bug prediction. In 2013 35th international conference on software engineering (ICSE) . IEEE, 392–401
Kim Herzig, Sascha Just, and Andreas Zeller. 2013 · 2013
Earlier work this paper cites.
Learning to generate pseudo-code from source code using statistical machine translation. In 2015 30th IEEE/ACM International Conference on Automated Software Engineering (ASE) . IEEE, 574–584
Yusuke Oda, Hiroyuki Fudaba, Graham Neubig, Hideaki Hata, Sakriani Sakti, Tomoki Toda, and Satoshi Nakamura. 2015 · 2015
Earlier work this paper cites.
The impact of mislabelling on the performance and interpretation of defect prediction models. In 2015 IEEE/ACM 37th IEEE International Conference on Software Engineering , Vol. 1. IEEE, 812–823
Chakkrit Tantithamthavorn, Shane McIntosh, Ahmed E Hassan, Akinori Ihara, and Kenichi Matsumoto. 2015 · 2015
Earlier work this paper cites.
Incorrect results in software engineering experiments: How to improve research practices
Magne Jørgensen, Tore Dybå, Knut Liestøl, and Dag IK Sjøberg. 2016 · 2016
Earlier work this paper cites.
Latent Predictor Networks for Code Generation. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) . 599–609
Wang Ling, Phil Blunsom, Edward Grefenstette, Karl Moritz Hermann, Tomáš Kočiskỳ, Fumin Wang, and Andrew Senior. 2016 · 2016
Earlier work this paper cites.
Sard: A software assurance reference dataset
Paul E Black. 2017 · 2017
Earlier work this paper cites.
Automatically generating commit messages from diffs using neural machine translation. In 2017 32nd IEEE/ACM International Conference on Automated Software Engineering (ASE) . IEEE, 135–146
Siyuan Jiang, Ameer Armaly, and Collin McMillan. 2017 · 2017
Earlier work this paper cites.
Automatically assessing code understandability: How far are we?. In 2017 32nd IEEE/ACM International Conference on Automated Software Engineering (ASE) . IEEE, 417–427
Simone Scalabrino, Gabriele Bavota, Christopher Vendome, Mario Linares-Vásquez, Denys Poshyvanyk, and Rocco Oliveto. 2017 · 2017
Earlier work this paper cites.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017 · 2017
Earlier work this paper cites.
Wild patterns: Ten years after the rise of adversarial machine learning. In Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security . 2154–2156
Battista Biggio and Fabio Roli. 2018 · 2018
Earlier work this paper cites.
Artificial intelligence faces reproducibility crisis
Matthew Hutson. 2018 · 2018
Earlier work this paper cites.
Reproducibility in scientific computing
Peter Ivie and Douglas Thain. 2018 · 2018
Earlier work this paper cites.
Neural-machine-translation-based commit message generation: how far are we?. In Proceedings of the 33rd ACM/IEEE International Conference on Automated Software Engineering . 373–384
Zhongxin Liu, Xin Xia, Ahmed E Hassan, David Lo, Zhenchang Xing, and Xinyu Wang. 2018 · 2018
Earlier work this paper cites.
Model compression via distillation and quantization. In International Conference on Learning Representations
Antonio Polino, Razvan Pascanu, and Dan Alistarh. 2018 · 2018
Earlier work this paper cites.
An experience report on defect modelling in practice: pitfalls and challenges. In Proceedings of the 40th International Conference on Software Engineering: Software Engineering in Practice, ICSE (SEIP) 2018, Gothenburg, Sweden, May 27 - June 03, 2018 , Frances Paulisch and Jan Bosch (Eds.). ACM, 286–295
Chakkrit Tantithamthavorn and Ahmed E. Hassan. 2018 · 2018
Earlier work this paper cites.
Improving automatic source code summarization via deep reinforcement learning. In Proceedings of the 33rd ACM/IEEE International Conference on Automated Software Engineering (ASE ’18) . Association for Computing Machinery, Montpellier France, 397–407
Yao Wan, Zhou Zhao, Min Yang, Guandong Xu, Haochao Ying, Jian Wu, and Philip S. Yu. 2018 · 2018
Earlier work this paper cites.
Learning to mine aligned code and natural language pairs from stack overflow. In Proceedings of the 15th international conference on mining software repositories . 476–486
Pengcheng Yin, Bowen Deng, Edgar Chen, Bogdan Vasilescu, and Graham Neubig. 2018 · 2018
Earlier work this paper cites.
Reliable benchmarking: requirements and solutions
Dirk Beyer, Stefan Löwe, and Philipp Wendler. 2019 · 2019
Earlier work this paper cites.
Machine learning applied to software testing: A systematic mapping study
Vinicius HS Durelli, Rafael S Durelli, Simone S Borges, Andre T Endo, Marcelo M Eler, Diego RC Dias, and Marcelo P Guimarães. 2019 · 2019
Earlier work this paper cites.
CCF Recommended List of International Conferences and Periodicals
China Computer Federation. 2023 · 2019
Earlier work this paper cites.
An empirical study towards characterizing deep learning development and deployment across different frameworks and platforms. In 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE) . IEEE, 810–822
Qianyu Guo, Sen Chen, Xiaofei Xie, Lei Ma, Qiang Hu, Hongtao Liu, Yang Liu, Jianjun Zhao, and Xiaohong Li. 2019 · 2019
Earlier work this paper cites.
When Code Completion Fails: A Case Study on Real-World Completions. In 2019 IEEE/ACM 41st International Conference on Software Engineering (ICSE) . IEEE, Montreal, QC, Canada, 960–970
Vincent J. Hellendoorn, Sebastian Proksch, Harald C. Gall, and Alberto Bacchelli. 2019 · 2019
Earlier work this paper cites.
Codesearchnet challenge: Evaluating the state of semantic code search
Hamel Husain, Ho-Hsiang Wu, Tiferet Gazit, Miltiadis Allamanis, and Marc Brockschmidt. 2019 · 2019
Earlier work this paper cites.
{ \{ TESSERACT } \} : Eliminating experimental bias in malware classification across space and time. In 28th USENIX Security Symposium (USENIX Security 19) . 729–746
Feargus Pendlebury, Fabio Pierazzi, Roberto Jordaney, Johannes Kinder, and Lorenzo Cavallaro. 2019 · 2019
Earlier work this paper cites.
Software product lines and variability modeling: A tertiary study
Mikko Raatikainen, Juha Tiihonen, and Tomi Männistö. 2019 · 2019
Earlier work this paper cites.
SoK: Benchmarking flaws in systems security. In 2019 IEEE European Symposium on Security and Privacy (EuroS&P) . IEEE, 310–325
Erik van der Kouwe, Gernot Heiser, Dennis Andriesse, Herbert Bos, and Cristiano Giuffrida. 2019 · 2019
Earlier work this paper cites.
Mining software defects: Should we consider affected releases?. In 2019 IEEE/ACM 41st International Conference on Software Engineering (ICSE) . IEEE, 654–665
Suraj Yatish, Jirayus Jiarpakdee, Patanamon Thongtanunam, and Chakkrit Tantithamthavorn. 2019 · 2019
Earlier work this paper cites.
AC/C++ code vulnerability dataset with code changes and CVE summaries. In Proceedings of the 17th International Conference on Mining Software Repositories . 508–512
Jiahao Fan, Yi Li, Shaohua Wang, and Tien N Nguyen. 2020 · 2020
Earlier work this paper cites.
A Performance-Sensitive Malware Detection System Using Deep Learning on Mobile Devices
Ruitao Feng, Sen Chen, Xiaofei Xie, Guozhu Meng, Shang-Wei Lin, and Yang Liu. 2021 · 2020
Earlier work this paper cites.
Codebert: A pre-trained model for programming and natural languages
Zhangyin Feng, Daya Guo, Duyu Tang, Nan Duan, Xiaocheng Feng, Ming Gong, Linjun Shou, Bing Qin, Ting Liu, Daxin Jiang, et al · 2020
Earlier work this paper cites.
Cc2vec: Distributed representations of code changes. In Proceedings of the ACM/IEEE 42nd International Conference on Software Engineering . 518–529
Thong Hoang, Hong Jin Kang, David Lo, and Julia Lawall. 2020 · 2020
Earlier work this paper cites.
Federated learning: Challenges, methods, and future directions
Tian Li, Anit Kumar Sahu, Ameet Talwalkar, and Virginia Smith. 2020 · 2020
Earlier work this paper cites.
Deep Learning Based Program Generation From Requirements Text: Are We There Yet?
Hui Liu, Mingzhu Shen, Jiaqi Zhu, Nan Niu, Ge Li, and Lu Zhang. 2022b · 2020
Earlier work this paper cites.
CD-VulD: Cross-Domain Vulnerability Discovery Based on Deep Domain Adaptation
Shigang Liu, Guanjun Lin, Lizhen Qu, Jun Zhang, Olivier De Vel, Paul Montague, and Yang Xiang. 2022a · 2020
Earlier work this paper cites.
Codebleu: a method for automatic evaluation of code synthesis
Shuo Ren, Daya Guo, Shuai Lu, Long Zhou, Shujie Liu, Duyu Tang, Neel Sundaresan, Ming Zhou, Ambrosio Blanco, and Shuai Ma. 2020 · 2020
Earlier work this paper cites.
Intellicode compose: Code generation using transformer. In Proceedings of the 28th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering . 1433–1443
Alexey Svyatkovskiy, Shao Kun Deng, Shengyu Fu, and Neel Sundaresan. 2020 · 2020
Earlier work this paper cites.
Reinforcement-Learning-Guided Source Code Summarization Using Hierarchical Attention
Wenhua Wang, Yuqun Zhang, Yulei Sui, Yao Wan, Zhou Zhao, Jian Wu, Philip S. Yu, and Guandong Xu. 2022d · 2020
Earlier work this paper cites.
Generating Adversarial Examples for Holding Robustness of Source Code Processing Models
Huangzhao Zhang, Zhuo Li, Ge Li, Lei Ma, Yang Liu, and Zhi Jin. 2020 · 2020
Earlier work this paper cites.
Program synthesis with large language models
Jacob Austin, Augustus Odena, Maxwell Nye, Maarten Bosma, Henryk Michalewski, David Dohan, Ellen Jiang, Carrie Cai, Michael Terry, Quoc Le, et al · 2021
Earlier work this paper cites.
Learning autocompletion from real-world datasets
Gareth Ari Aye, Seohyun Kim, and Hongyu Li. 2021a · 2021
Earlier work this paper cites.
Learning autocompletion from real-world datasets. In 2021 IEEE/ACM 43rd International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP) . IEEE, 131–139
Gareth Ari Aye, Seohyun Kim, and Hongyu Li. 2021b · 2021
Earlier work this paper cites.
Extracting training data from large language models. In 30th USENIX Security Symposium (USENIX Security 21) . 2633–2650
Nicholas Carlini, Florian Tramer, Eric Wallace, Matthew Jagielski, Ariel Herbert-Voss, Katherine Lee, Adam Roberts, Tom Brown, Dawn Song, Ulfar Erlingsson, et al · 2021
Earlier work this paper cites.
Deep learning based vulnerability detection: Are we there yet
Saikat Chakraborty, Rahul Krishna, Yangruibo Ding, and Baishakhi Ray. 2021 · 2021
Earlier work this paper cites.
Evaluating large language models trained on code
Mark Chen, Jerry Tworek, Heewoo Jun, Qiming Yuan, Henrique Ponde de Oliveira Pinto, Jared Kaplan, Harri Edwards, Yuri Burda, Nicholas Joseph, Greg Brockman, et al · 2021
Earlier work this paper cites.
Empirical study of transformers for source code. In Proceedings of the 29th ACM joint meeting on European software engineering conference and symposium on the foundations of software engineering . 703–715
Nadezhda Chirkova and Sergey Troshin. 2021 · 2021
Earlier work this paper cites.
Explaining mispredictions of machine learning models using rule induction. In Proceedings of the 29th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE 2021) . Association for Computing Machinery, New York, NY, USA, 716–727
Jürgen Cito, Isil Dillig, Seohyun Kim, Vijayaraghavan Murali, and Satish Chandra. 2021 · 2021
Earlier work this paper cites.
Patching as translation: the data and the metaphor. In Proceedings of the 35th IEEE/ACM International Conference on Automated Software Engineering (ASE ’20) . Association for Computing Machinery, Virtual Event Australia, 275–286
Yangruibo Ding, Baishakhi Ray, Premkumar Devanbu, and Vincent J. Hellendoorn. 2021 · 2021
Earlier work this paper cites.
Compressing large-scale transformer-based models: A case study on bert
Prakhar Ganesh, Yao Chen, Xin Lou, Mohammad Ali Khan, Yin Yang, Hassan Sajjad, Preslav Nakov, Deming Chen, and Marianne Winslett. 2021 · 2021
Earlier work this paper cites.
Robustness of on-device models: Adversarial attack to deep learning models on android apps. In 2021 IEEE/ACM 43rd International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP) . IEEE, 101–110
Yujin Huang, Han Hu, and Chunyang Chen. 2021 · 2021
Earlier work this paper cites.
Practitioners’ Perceptions of the Goals and Visual Explanations of Defect Prediction Models. In 18th IEEE/ACM International Conference on Mining Software Repositories, MSR 2021, Madrid, Spain, May 17-19, 2021 . IEEE, 432–443
Jirayus Jiarpakdee, Chakkrit Tantithamthavorn, and John C. Grundy. 2021 · 2021
Earlier work this paper cites.
Predicting unstable software benchmarks using static source code features
Christoph Laaber, Mikael Basmaci, and Pasquale Salza. 2021 · 2021
Earlier work this paper cites.
Vulnerability detection with fine-grained interpretations. In Proceedings of the 29th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE 2021) . Association for Computing Machinery, New York, NY, USA, 292–303
Yi Li, Shaohua Wang, and Tien N. Nguyen. 2021 · 2021
Earlier work this paper cites.
On the Reproducibility and Replicability of Deep Learning in Software Engineering
Chao Liu, Cuiyun Gao, Xin Xia, David Lo, John Grundy, and Xiaohu Yang. 2021a · 2021
Earlier work this paper cites.
Can Neural Clone Detection Generalize to Unseen Functionalities. In 2021 36th IEEE/ACM International Conference on Automated Software Engineering (ASE) . IEEE, Melbourne, Australia, 617–629
Chenyao Liu, Zeqi Lin, Jian-Guang Lou, Lijie Wen, and Dongmei Zhang. 2021b · 2021
Cited alongside, same era.
CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and Generation. In Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 1)
Shuai Lu, Daya Guo, Shuo Ren, Junjie Huang, Alexey Svyatkovskiy, Ambrosio Blanco, Colin Clement, Dawn Drain, Daxin Jiang, Duyu Tang, et al · 2021
Cited alongside, same era.
Recent advances in natural language processing via large pre-trained language models: A survey
Bonan Min, Hayley Ross, Elior Sulem, Amir Pouran Ben Veyseh, Thien Huu Nguyen, Oscar Sainz, Eneko Agirre, Ilana Heintz, and Dan Roth. 2021 · 2021
Cited alongside, same era.
CrossVul: a cross-language vulnerability dataset with commit data. In Proceedings of the 29th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering . 1565–1569
Georgios Nikitopoulos, Konstantina Dritsa, Panos Louridas, and Dimitris Mitropoulos. 2021 · 2021
Getting started with Claude
Anthropic. 2023 · 2023
Closest in time.
Improving code generation by training with natural language feedback
Angelica Chen, Jérémy Scheurer, Tomasz Korbak, Jon Ander Campos, Jun Shern Chan, Samuel R Bowman, Kyunghyun Cho, and Ethan Perez. 2023 · 2023
Closest in time.
CodeBPE: Investigating Subtokenization Options for Large Language Model Pretraining on Source Code
Nadezhda Chirkova and Sergey Troshin. 2023 · 2023
Closest in time.
Expert Perspectives on Explainability
Jürgen Cito, Satish Chandra, Chakkrit Tantithamthavorn, and Hadi Hemmati. 2023 · 2023
Closest in time.
Github copilot ai pair programmer: Asset or liability?
Arghavan Moradi Dakhel, Vahid Majdinasab, Amin Nikanjam, Foutse Khomh, Michel C Desmarais, and Zhen Ming Jack Jiang. 2023 · 2023
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Confident learning: Estimating uncertainty in dataset labels
Curtis Northcutt, Lu Jiang, and Isaac Chuang. 2021 · 2021
Cited alongside, same era.
Thinking Like a Developer? Comparing the Attention of Humans with Neural Models of Code. In 2021 36th IEEE/ACM International Conference on Automated Software Engineering (ASE) . IEEE, Melbourne, Australia, 867–879
Matteo Paltenghi and Michael Pradel. 2021 · 2021
Cited alongside, same era.
On the generalizability of Neural Program Models with respect to semantic-preserving program transformations
Md Rafiqul Islam Rabin, Nghi DQ Bui, Ke Wang, Yijun Yu, Lingxiao Jiang, and Mohammad Amin Alipour. 2021 · 2021
Cited alongside, same era.
Reassessing automatic evaluation metrics for code summarization tasks. In Proceedings of the 29th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering . 1105–1116
Devjeet Roy, Sarah Fakhoury, and Venera Arnaoudova. 2021 · 2021
Cited alongside, same era.
You autocomplete me: Poisoning vulnerabilities in neural code completion. In 30th USENIX Security Symposium (USENIX Security 21) . 1559–1575
Roei Schuster, Congzheng Song, Eran Tromer, and Vitaly Shmatikov. 2021 · 2021
Cited alongside, same era.
Fast and memory-efficient neural code completion. In 2021 IEEE/ACM 18th International Conference on Mining Software Repositories (MSR) . IEEE, 329–340
Alexey Svyatkovskiy, Sebastian Lee, Anna Hadjitofi, Maik Riechert, Juliana Vicente Franco, and Miltiadis Allamanis. 2021 · 2021
Cited alongside, same era.
Explainable AI for Software Engineering. In 36th IEEE/ACM International Conference on Automated Software Engineering, ASE 2021, Melbourne, Australia, November 15-19, 2021 . IEEE, 1–2
Chakkrit Tantithamthavorn and Jirayus Jiarpakdee. 2021 · 2021
Cited alongside, same era.
Yue Wang, Weishi Wang, Shafiq Joty, and Steven CH Hoi. 2021 · 2021
Cited alongside, same era.
Revisiting Learning-based Commit Message Generation. In 2023 IEEE/ACM 45th International Conference on Software Engineering (ICSE) . IEEE, 794–805
Jinhao Dong, Yiling Lou, Dan Hao, and Lin Tan. 2023 · 2023
Closest in time.
Automated repair of programs from large language models. In 2023 IEEE/ACM 45th International Conference on Software Engineering (ICSE) . IEEE, 1469–1481
Zhiyu Fan, Xiang Gao, Martin Mirchev, Abhik Roychoudhury, and Shin Hwei Tan. 2023 · 2023
Closest in time.
RepresentThemAll: A Universal Learning Representation of Bug Reports. In 2023 IEEE/ACM 45th International Conference on Software Engineering (ICSE) . IEEE, 602–614
Sen Fang, Tao Zhang, Youshuai Tan, He Jiang, Xin Xia, and Xiaobing Sun. 2023 · 2023
Closest in time.
VulExplainer: A Transformer-based Hierarchical Distillation for Explaining Vulnerability Types
Michael Fu, Chakkrit Tantithamthavorn Van Nguyen, Trung Le, and Dinh Phung. 2023 · 2023
Closest in time.
Keeping Pace with Ever-Increasing Data: Towards Continual Learning of Code Intelligence Models
Shuzheng Gao, Hongyu Zhang, Cuiyun Gao, and Chaozheng Wang. 2023b · 2023
Closest in time.
Github copilot
GitHub. 2023 · 2023
Closest in time.
Build generative AI applications with Google
Google. 2023 · 2023
Closest in time.
Yiling He, Jian Lou, Zhan Qin, and Kui Ren. 2023 · 2023
Closest in time.
Large Language Models for Software Engineering: A Systematic Literature Review
Xinyi Hou, Yanjie Zhao, Yue Liu, Zhou Yang, Kailong Wang, Li Li, Xiapu Luo, David Lo, John Grundy, and Haoyu Wang. 2023 · 2023
Closest in time.
Detecting Temporal Inconsistency in Biased Datasets for Android Malware Detection. In The 6th International Workshop on Advances in Mobile App Analysis (A-Mobile) . 0–0
Haonan Hu, Yue Liu, Yanjie Zhao, Yonghui Liu, Xiaoyu Sun, Chakkrit Tantithamthavorn, and Li Li. 2023 · 2023
Closest in time.
Impact of code language models on automated program repair
Nan Jiang, Kevin Liu, Thibaud Lutellier, and Lin Tan. 2023a · 2023
Closest in time.
An Empirical Study of Pre-Trained Model Reuse in the Hugging Face Deep Learning Model Registry
Wenxin Jiang, Nicholas Synovic, Matt Hyatt, Taylor R. Schorlemmer, Rohan Sethi, Yung-Hsiang Lu, George K. Thiruvathukal, and James C. Davis. 2023b · 2023
Closest in time.
RU-SURE? Uncertainty-Aware Code Suggestions By Maximizing Utility Across Random User Intents
Daniel D Johnson, Daniel Tarlow, and Christian Walder. 2023 · 2023
Closest in time.
Challenges and applications of large language models
Jean Kaddour, Joshua Harris, Maximilian Mozes, Herbie Bradley, Roberta Raileanu, and Robert McHardy. 2023 · 2023
Closest in time.
A watermark for large language models
John Kirchenbauer, Jonas Geiping, Yuxin Wen, Jonathan Katz, Ian Miers, and Tom Goldstein. 2023 · 2023
Closest in time.
Trustworthy AI: From principles to practices
Bo Li, Peng Qi, Bo Liu, Shuai Di, Jingen Liu, Jiquan Pei, Jinfeng Yi, and Bowen Zhou. 2023c · 2023
Closest in time.
StarCoder: may the source be with you!
Raymond Li, Loubna Ben Allal, Yangtian Zi, Niklas Muennighoff, Denis Kocetkov, Chenghao Mou, Marc Marone, Christopher Akiki, Jia Li, Jenny Chim, et al · 2023
Closest in time.
Multi-target Backdoor Attacks for Code Pre-trained Models
Yanzhou Li, Shangqing Liu, Kangjie Chen, Xiaofei Xie, Tianwei Zhang, and Yang Liu. 2023b · 2023
Closest in time.
Cctest: Testing and repairing code completion systems. In 2023 IEEE/ACM 45th International Conference on Software Engineering (ICSE) . IEEE, 1238–1250
Zongjie Li, Chaozheng Wang, Zhibo Liu, Haoxuan Wang, Dong Chen, Shuai Wang, and Cuiyun Gao. 2023d · 2023
Closest in time.
On the feasibility of specialized ability stealing for large language code models
Zongjie Li, Chaozheng Wang, Pingchuan Ma, Chaowei Liu, Shuai Wang, Daoyuan Wu, and Cuiyun Gao. 2023e · 2023
Closest in time.
Contrabert: Enhancing code pre-trained models via contrastive learning
Shangqing Liu, Bozhi Wu, Xiaofei Xie, Guozhu Meng, and Yang Liu. 2023c · 2023
Closest in time.
Refining ChatGPT-Generated Code: Characterizing and Mitigating Code Quality Issues
Yue Liu, Thanh Le-Cong, Ratnadira Widyasari, Chakkrit Tantithamthavorn, Li Li, Xuan-Bach D Le, and David Lo. 2023a · 2023
Closest in time.
Towards More Realistic Evaluation for Neural Test Oracle Generation
Zhongxin Liu, Kui Liu, Xin Xia, and Xiaohu Yang. 2023b · 2023
Closest in time.
Trustworthy and Synergistic Artificial Intelligence for Software Engineering: Vision and Roadmaps
David Lo. 2023 · 2023
Closest in time.
Junyi Lu, Lei Yu, Xiaojia Li, Li Yang, and Chun Zuo. 2023 · 2023
Closest in time.
Analyzing Leakage of Personally Identifiable Information in Language Models. In 2023 IEEE Symposium on Security and Privacy (SP) . IEEE Computer Society, 346–363
Nils Lukas, Ahmed Salem, Robert Sim, Shruti Tople, Lukas Wutschitz, and Santiago Zanella-Béguelin. 2023 · 2023
Closest in time.
On the robustness of code generation techniques: An empirical study on github copilot
Antonio Mastropaolo, Luca Pascarella, Emanuela Guglielmi, Matteo Ciniselli, Simone Scalabrino, Rocco Oliveto, and Gabriele Bavota. 2023 · 2023
Closest in time.
Template-based Neural Program Repair. In 2023 IEEE/ACM 45th International Conference on Software Engineering (ICSE) . IEEE, 1456–1468
Xiangxin Meng, Xu Wang, Hongyu Zhang, Hailong Sun, Xudong Liu, and Chunming Hu. 2023 · 2023
Closest in time.
When to Show a Suggestion? Integrating Human Feedback in AI-Assisted Programming
Hussein Mozannar, Gagan Bansal, Adam Fourney, and Eric Horvitz. 2023 · 2023
Closest in time.
Understanding and Tackling Label Errors in Deep Learning-Based Vulnerability Detection (Experience Paper). In Proceedings of the 32nd ACM SIGSOFT International Symposium on Software Testing and Analysis . 52–63
Xu Nie, Ningke Li, Kailong Wang, Shangguang Wang, Xiapu Luo, and Haoyu Wang. 2023 · 2023
Closest in time.
An empirical comparison of pre-trained models of source code
Changan Niu, Chuanyi Li, Vincent Ng, Dongxiao Chen, Jidong Ge, and Bin Luo. 2023b · 2023
Closest in time.
CrossCodeBench: Benchmarking Cross-Task Generalization of Source Code Models
Changan Niu, Chuanyi Li, Vincent Ng, and Bin Luo. 2023a · 2023
Closest in time.
Demystifying GPT Self-Repair for Code Generation
Theo X Olausson, Jeevana Priya Inala, Chenglong Wang, Jianfeng Gao, and Armando Solar-Lezama. 2023 · 2023
Closest in time.
I know what you trained last summer: A survey on stealing machine learning models and defences
Daryna Oliynyk, Rudolf Mayer, and Andreas Rauber. 2023 · 2023
Closest in time.
OpenAI. 2023 · 2023
Closest in time.
Examining Zero-Shot Vulnerability Repair with Large Language Models. In 2023 IEEE Symposium on Security and Privacy (SP) . 2339–2356
Hammond Pearce, Benjamin Tan, Baleegh Ahmad, Ramesh Karri, and Brendan Dolan-Gavitt. 2023 · 2023
Closest in time.
CORE Rankings Portal
The Computing Research and Education Association of Australasia. 2023 · 2023
Closest in time.
Representation Bias in Data: A Survey on Identification and Resolution Techniques
Nima Shahbazi, Yin Lin, Abolfazl Asudeh, and HV Jagadish. 2023 · 2023
Closest in time.
Towards Efficient Fine-tuning of Pre-trained Code Models: An Experimental Study and Beyond
Ensheng Shi, Yanlin Wang, Hongyu Zhang, Lun Du, Shi Han, Dongmei Zhang, and Hongbin Sun. 2023a · 2023
Closest in time.
Smaller, Faster, Greener: Compressing Pre-trained Code Models via Surrogate-Assisted Optimization
Jieke Shi, Zhou Yang, Hong Jin Kang, Bowen Xu, Junda He, and David Lo. 2023b · 2023
Closest in time.
An empirical study of deep learning models for vulnerability detection. In 2023 IEEE/ACM 45th International Conference on Software Engineering (ICSE) . IEEE, 2237–2248
Benjamin Steenhoek, Md Mahbubur Rahman, Richard Jiles, and Wei Le. 2023 · 2023
Closest in time.
Backdooring Neural Code Search
Weisong Sun, Yuchen Chen, Guanhong Tao, Chunrong Fang, Xiangyu Zhang, Quanjun Zhang, and Bin Luo. 2023a · 2023
Closest in time.
CodeMark: Imperceptible Watermarking for Code Datasets against Neural Code Completion Models
Zhensu Sun, Xiaoning Du, Fu Song, and Li Li. 2023b · 2023
Closest in time.
Explainable AI for SE: Challenges and Future Directions
Chakkrit Tantithamthavorn, Jürgen Cito, Hadi Hemmati, and Satish Chandra. 2023 · 2023
Closest in time.
On the robustness of chatgpt: An adversarial and out-of-distribution perspective
Jindong Wang, Xixu Hu, Wenxin Hou, Hao Chen, Runkai Zheng, Yidong Wang, Linyi Yang, Haojun Huang, Wei Ye, Xiubo Geng, et al · 2023
Closest in time.
Software Testing with Large Language Model: Survey, Landscape, and Vision
Junjie Wang, Yuchao Huang, Chunyang Chen, Zhe Liu, Song Wang, and Qing Wang. 2023b · 2023
Closest in time.
Yuxiang Wei, Chunqiu Steven Xia, and Lingming Zhang. 2023 · 2023
Closest in time.
Exploring Parameter-Efficient Fine-Tuning Techniques for Code Generation with Large Language Models
Martin Weyssow, Xin Zhou, Kisub Kim, David Lo, and Houari Sahraoui. 2023 · 2023
Closest in time.
How Effective Are Neural Networks for Fixing Security Vulnerabilities
Yi Wu, Nan Jiang, Hung Viet Pham, Thibaud Lutellier, Jordan Davis, Lin Tan, Petr Babkin, and Sameena Shah. 2023 · 2023
Closest in time.
Automated program repair in the era of large pre-trained language models. In Proceedings of the 45th International Conference on Software Engineering (ICSE 2023). Association for Computing Machinery
Chunqiu Steven Xia, Yuxiang Wei, and Lingming Zhang. 2023 · 2023
Closest in time.
Does data sampling improve deep learning-based vulnerability detection? Yeas! and Nays!. In 2023 IEEE/ACM 45th International Conference on Software Engineering (ICSE) . IEEE, 2287–2298
Xu Yang, Shaowei Wang, Yi Li, and Shaohua Wang. 2023a · 2023
Closest in time.
Slice-Based Code Change Representation Learning. In 2023 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER) . IEEE, 319–330
Fengyi Zhang, Bihuan Chen, Yufei Zhao, and Xin Peng. 2023a · 2023
Closest in time.
Challenging Machine Learning-Based Clone Detectors via Semantic-Preserving Code Transformations
Weiwei Zhang, Shengjian Guo, Hongyu Zhang, Yulei Sui, Yinxing Xue, and Yun Xu. 2023b · 2023
Closest in time.
ModelObfuscator: Obfuscating Model Information to Protect Deployed ML-Based Systems
Mingyi Zhou, Xiang Gao, Jing Wu, John Grundy, Xiao Chen, Chunyang Chen, and Li Li. 2023 · 2023
Closest in time.