Fetching the paper…
Reading the bibliography…
Identifying the point of error is imperative in software debugging.
Language Models are Few-Shot Learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 1901
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 · 1909
Earlier work this paper cites.
An Empirical Investigation of the Relationship Between Spectra Differences and Regression Faults
Mary Jean Harrold, Gregg Rothermel, Kent Sayre, Rui Wu, and Liu Yi. 2000 · 2000
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 · 2002
Earlier work this paper cites.
Visualization of Test Information to Assist Fault Localization. In Proceedings of the 24th International Conference on Software Engineering . 467–477
James A Jones, Mary Jean Harrold, and John Stasko. 2002 · 2002
Earlier work this paper cites.
An Evaluation of Similarity Coefficients for Software Fault Localization. In 2006 12th Pacific Rim International Symposium on Dependable Computing (PRDC’06) . IEEE, 39–46
Rui Abreu, Peter Zoeteweij, and Arjan JC Van Gemund. 2006 · 2006
Earlier work this paper cites.
Improving Test Suites for Efficient Fault Localization. In Proceedings of the 28th International Conference on Software Engineering . 82–91
Benoit Baudry, Franck Fleurey, and Yves Le Traon. 2006 · 2006
Earlier work this paper cites.
MuJava: A Mutation System for Java. In Proceedings of the 28th International Conference on Software Engineering . 827–830
Yu-Seung Ma, Jeff Offutt, and Yong-Rae Kwon. 2006 · 2006
Earlier work this paper cites.
On the Accuracy of Spectrum-based Fault Localization. In Testing: Academic and Industrial Conference Practice and Research Techniques-MUTATION (TAICPART-MUTATION 2007) . IEEE, 89–98
Rui Abreu, Peter Zoeteweij, and Arjan JC Van Gemund. 2007 · 2007
Earlier work this paper cites.
Efficient Top-k Query Evaluation on Probabilistic Data. In 2007 IEEE 23rd International Conference on Data Engineering . IEEE, 886–895
Christopher Re, Nilesh Dalvi, and Dan Suciu. 2006 · 2007
Earlier work this paper cites.
Effective Fault Localization using Code Coverage. In 31st Annual International Computer Software and Applications Conference (COMPSAC 2007) , Vol. 1. IEEE, 449–456
W Eric Wong, Yu Qi, Lei Zhao, and Kai-Yuan Cai. 2007 · 2007
Earlier work this paper cites.
A practical evaluation of spectrum-based fault localization
Rui Abreu, Peter Zoeteweij, Rob Golsteijn, and Arjan JC Van Gemund. 2009b · 2009
Earlier work this paper cites.
Spectrum-Based Multiple Fault Localization. In 2009 IEEE/ACM International Conference on Automated Software Engineering . IEEE, 88–99
Rui Abreu, Peter Zoeteweij, and Arjan JC Van Gemund. 2009a · 2009
Earlier work this paper cites.
A Model for Spectra-Based Software Diagnosis
Lee Naish, Hua Jie Lee, and Kotagiri Ramamohanarao. 2011 · 2011
Earlier work this paper cites.
GZoltar: An Eclipse Plug-In for Testing and Debugging. In Proceedings of the 27th IEEE/ACM International Conference on Automated Software Engineering . 378–381
José Campos, André Riboira, Alexandre Perez, and Rui Abreu. 2012 · 2012
Earlier work this paper cites.
Sequence Transduction with Recurrent Neural Networks
Alex Graves. 2012 · 2012
Earlier work this paper cites.
Evolving Human Competitive Spectra-Based Fault Localisation Techniques. In Search Based Software Engineering: 4th International Symposium, SSBSE 2012, Riva del Garda, Italy, September 28-30, 2012. Proceedings 4 . Springer, 244–258
Shin Yoo. 2012 · 2012
Earlier work this paper cites.
The DStar Method for Effective Software Fault Localization
W Eric Wong, Vidroha Debroy, Ruizhi Gao, and Yihao Li. 2013 · 2013
Earlier work this paper cites.
The Major Mutation Framework: Efficient and Scalable Mutation Analysis for Java. In Proceedings of the 2014 International Symposium on Software Testing and Analysis . 433–436
René Just. 2014 · 2014
Earlier work this paper cites.
Defects4J: A Database of Existing Faults to Enable Controlled Testing Studies for Java Programs. In Proceedings of the 2014 International Symposium on Software Testing and Analysis . 437–440
René Just, Darioush Jalali, and Michael D Ernst. 2014 · 2014
Earlier work this paper cites.
Ask the Mutants: Mutating Faulty Programs for Fault Localization. In 2014 IEEE Seventh International Conference on Software Testing, Verification and Validation . IEEE, 153–162
Seokhyeon Moon, Yunho Kim, Moonzoo Kim, and Shin Yoo. 2014 · 2014
Earlier work this paper cites.
New Initiative: The Naturalness of Software. In 2015 IEEE/ACM 37th IEEE International Conference on Software Engineering , Vol. 2. IEEE, 543–546
Premkumar Devanbu. 2015 · 2015
Earlier work this paper cites.
Metallaxis-FL: mutation-based fault localization
Mike Papadakis and Yves Le Traon. 2015 · 2015
Earlier work this paper cites.
A Learning-to-Rank Based Fault Localization Approach using Likely Invariants. In Proceedings of the 25th International Symposium on Software Testing and Analysis . 177–188
Tien-Duy B. Le, David Lo, Claire Le Goues, and Lars Grunske. 2016 · 2016
Earlier work this paper cites.
PIT: A Practical Mutation Testing Tool for Java. In Proceedings of the 25th International Symposium on Software Testing and Analysis . 449–452
Henry Coles, Thomas Laurent, Christopher Henard, Mike Papadakis, and Anthony Ventresque. 2016 · 2016
Earlier work this paper cites.
A Survey on Software Fault Localization
W Eric Wong, Ruizhi Gao, Yihao Li, Rui Abreu, and Franz Wotawa. 2016 · 2016
Earlier work this paper cites.
QuixBugs: A Multi-Lingual Program Repair Benchmark Set Based on the Quixey Challenge. In Proceedings Companion of the 2017 ACM SIGPLAN International Conference on Systems, Programming, Languages, and Applications: Software for Humanity . 55–56
Derrick Lin, James Koppel, Angela Chen, and Armando Solar-Lezama. 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
Cited alongside, same era.
Deep Learning-Based Fault Localization with Contextual Information
Zhuo Zhang, Yan Lei, Qingping Tan, Xiaoguang Mao, Ping Zeng, and Xi Chang. 2017 · 2017
Cited alongside, same era.
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018 · 2018
Cited alongside, same era.
SPT-Code: Sequence-to-Sequence Pre-Training for Learning Source Code Representations. In Proceedings of the 44th International Conference on Software Engineering . 2006–2018
Changan Niu, Chuanyi Li, Vincent Ng, Jidong Ge, Liguo Huang, and Bin Luo. 2022 · 2018
Cited alongside, same era.
Improving Language Understanding by Generative Pre-Training
Alec Radford, Karthik Narasimhan, Tim Salimans, Ilya Sutskever, et al · 2018
Cited alongside, same era.
InCoder: A Generative Model for Code Infilling and Synthesis. In The Eleventh International Conference on Learning Representations
Daniel Fried, Armen Aghajanyan, Jessy Lin, Sida Wang, Eric Wallace, Freda Shi, Ruiqi Zhong, Scott Yih, Luke Zettlemoyer, and Mike Lewis. 2022 · 2022
Later among the works it cites.
UniXcoder: Unified Cross-Modal Pre-training for Code Representation. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) . 7212–7225
Daya Guo, Shuai Lu, Nan Duan, Yanlin Wang, Ming Zhou, and Jian Yin. 2022 · 2022
Later among the works it cites.
The Stack: 3 TB of permissively licensed source code
Denis Kocetkov, Raymond Li, LI Jia, Chenghao Mou, Yacine Jernite, Margaret Mitchell, Carlos Muñoz Ferrandis, Sean Hughes, Thomas Wolf, Dzmitry Bahdanau, et al · 2022
Later among the works it cites.
Competition-Level Code Generation with AlphaCode
Yujia Li, David Choi, Junyoung Chung, Nate Kushman, Julian Schrittwieser, Rémi Leblond, Tom Eccles, James Keeling, Felix Gimeno, Agustin Dal Lago, et al · 2022
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
How Much Time Do Developers Spend Actually Writing Code?
Chris Grams. 2019 · 2019
Cited alongside, same era.
Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Ves Stoyanov, and Luke Zettlemoyer. 2019 · 2019
Cited alongside, same era.
DeepFL: Integrating Multiple Fault Diagnosis Dimensions for Deep Fault Localization. In Proceedings of the 28th ACM SIGSOFT International Symposium on Software Testing and Analysis . 169–180
Xia Li, Wei Li, Yuqun Zhang, and Lingming Zhang. 2019 · 2019
Cited alongside, same era.
CNN-FL: An Effective Approach for Localizing Faults using Convolutional Neural Networks. In 2019 IEEE 26th International Conference on Software Analysis, Evolution and Reengineering (SANER) . IEEE, 445–455
Zhuo Zhang, Yan Lei, Xiaoguang Mao, and Panpan Li. 2019 · 2019
Cited alongside, same era.
The Pile: An 800GB Dataset of Diverse Text for Language Modeling
Leo Gao, Stella Biderman, Sid Black, Laurence Golding, Travis Hoppe, Charles Foster, Jason Phang, Horace He, Anish Thite, Noa Nabeshima, et al · 2020
Cited alongside, same era.
GraphCodeBERT: Pre-training Code Representations with Data Flow. In International Conference on Learning Representations
Daya Guo, Shuo Ren, Shuai Lu, Zhangyin Feng, Duyu Tang, LIU Shujie, Long Zhou, Nan Duan, Alexey Svyatkovskiy, Shengyu Fu, et al · 2020
Cited alongside, same era.
Learning and Evaluating Contextual Embedding of Source Code. In International Conference on Machine Learning . PMLR, 5110–5121
Aditya Kanade, Petros Maniatis, Gogul Balakrishnan, and Kensen Shi. 2020 · 2020
Cited alongside, same era.
Improving Fault Localization and Program Repair with Deep Semantic Features and Transferred Knowledge. In Proceedings of the 44th International Conference on Software Engineering . 1169–1180
Xiangxin Meng, Xu Wang, Hongyu Zhang, Hailong Sun, and Xudong Liu. 2022 · 2022
Later among the works it cites.
CodeGen: An Open Large Language Model for Code with Multi-Turn Program Synthesis. In The Eleventh International Conference on Learning Representations
Erik Nijkamp, Bo Pang, Hiroaki Hayashi, Lifu Tu, Huan Wang, Yingbo Zhou, Silvio Savarese, and Caiming Xiong. 2022 · 2022
Later among the works it cites.
Chenze Shao and Yang Feng. 2022 · 2022
Later among the works it cites.
Learning to Construct Better Mutation Faults. In Proceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering . 1–13
Zhao Tian, Junjie Chen, Qihao Zhu, Junjie Yang, and Lingming Zhang. 2022 · 2022
Later among the works it cites.
XAI4FL: Enhancing Spectrum-Based Fault Localization with Explainable Artificial Intelligence. In Proceedings of the 30th IEEE/ACM International Conference on Program Comprehension . 499–510
Ratnadira Widyasari, Gede Artha Azriadi Prana, Stefanus A Haryono, Yuan Tian, Hafil Noer Zachiary, and David Lo. 2022 · 2022
Later among the works it cites.
Less Training, More Repairing Please: Revisiting Automated Program Repair via Zero-Shot Learning. In Proceedings of the 30th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering . 959–971
Chunqiu Steven Xia and Lingming Zhang. 2022 · 2022
Later among the works it cites.
Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al · 2023
Later among the works it cites.
Parameter-efficient fine-tuning of large-scale pre-trained language models
Ning Ding, Yujia Qin, Guang Yang, Fuchao Wei, Zonghan Yang, Yusheng Su, Shengding Hu, Yulin Chen, Chi-Min Chan, Weize Chen, et al · 2023
Later among the works it cites.
Large Language Models for Software Engineering: Survey and Open Problems. In 2023 IEEE/ACM International Conference on Software Engineering: Future of Software Engineering (ICSE-FoSE) . IEEE Computer Society, 31–53
Angela Fan, Beliz Gokkaya, Mark Harman, Mitya Lyubarskiy, Shubho Sengupta, Shin Yoo, and Jie M Zhang. 2023 · 2023
Later among the works it cites.
LongCoder: A Long-Range Pre-trained Language Model for Code Completion. In International Conference on Machine Learning . PMLR, 12098–12107
Daya Guo, Canwen Xu, Nan Duan, Jian Yin, and Julian McAuley. 2023 · 2023
Later among the works it cites.
An Empirical Study on Fine-Tuning Large Language Models of Code for Automated Program Repair. In 2023 38th IEEE/ACM International Conference on Automated Software Engineering (ASE) . IEEE, 1162–1174
Kai Huang, Xiangxin Meng, Jian Zhang, Yang Liu, Wenjie Wang, Shuhao Li, and Yuqing Zhang. 2023 · 2023
Later among the works it cites.
Impact of Code Language Models on Automated Program Repair. In 2023 IEEE/ACM 45th International Conference on Software Engineering (ICSE) . IEEE, 1430–1442
Nan Jiang, Kevin Liu, Thibaud Lutellier, and Lin Tan. 2023 · 2023
Later among the works it cites.
StarCoder: may the source be with you!
Raymond Li, Yangtian Zi, Niklas Muennighoff, Denis Kocetkov, Chenghao Mou, Marc Marone, Christopher Akiki, LI Jia, Jenny Chim, Qian Liu, et al · 2023
Later among the works it cites.
Code Llama: Open Foundation Models for Code
Baptiste Roziere, Jonas Gehring, Fabian Gloeckle, Sten Sootla, Itai Gat, Xiaoqing Ellen Tan, Yossi Adi, Jingyu Liu, Tal Remez, Jérémy Rapin, et al · 2023
Later among the works it cites.
CodeT5+: Open Code Large Language Models for Code Understanding and Generation. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing . 1069–1088
Yue Wang, Hung Le, Akhilesh Gotmare, Nghi Bui, Junnan Li, and Steven Hoi. 2023 · 2023
Later among the works it cites.
GMBFL: Optimizing Mutation-Based Fault Localization via Graph Representation. In 2023 IEEE International Conference on Software Maintenance and Evolution (ICSME) . IEEE, 245–257
Shumei Wu, Zheng Li, Yong Liu, Xiang Chen, and Mingyu Li. 2023 · 2023
Later among the works it cites.
A Survey of Learning-based Automated Program Repair
Quanjun Zhang, Chunrong Fang, Yuxiang Ma, Weisong Sun, and Zhenyu Chen. 2023 · 2023
Later among the works it cites.
A Survey of Large Language Models
Wayne Xin Zhao, Kun Zhou, Junyi Li, Tianyi Tang, Xiaolei Wang, Yupeng Hou, Yingqian Min, Beichen Zhang, Junjie Zhang, Zican Dong, et al · 2023
Later among the works it cites.
CodeGeeX: A Pre-Trained Model for Code Generation with Multilingual Evaluations on HumanEval-X
Qinkai Zheng, Xiao Xia, Xu Zou, Yuxiao Dong, Shan Wang, Yufei Xue, Zihan Wang, Lei Shen, Andi Wang, Yang Li, et al · 2023
Later among the works it cites.
A Survey on Large Language Models for Code Generation
Juyong Jiang, Fan Wang, Jiasi Shen, Sungju Kim, and Sunghun Kim. 2024 · 2024
Closest in time.
AutoCoder: Enhancing Code Large Language Model with \ \backslash textsc { \{ AIEV-Instruct } \}
Bin Lei, Yuchen Li, and Qiuwu Chen. 2024 · 2024
Closest in time.
Large Language Models for Test-Free Fault Localization. In Proceedings of the 46th IEEE/ACM International Conference on Software Engineering . 1–12
Aidan ZH Yang, Claire Le Goues, Ruben Martins, and Vincent Hellendoorn. 2024 · 2024
Closest in time.