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Software debugging is a time-consuming endeavor involving a series of steps, such as fault localization and patch generation, each requiring thorough analysis and a deep understanding of the underlying logic.
Towards a model of programmers’ cognitive processes in software maintenance: A structural learning theory approach for debugging
David P. Hale and Dwight A. Haworth. 1991 · 1991
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An evaluation of the cognitive processes of programmers engaged in software debugging
Joanne E. Hale, Shane Sharpe, and David P. Hale. 1999 · 1999
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The pragmatic programmer: From journeyman to master
Hunt Andrew and Thomas David. 2000 · 2000
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An evaluation of similarity coefficients for software fault localization
Rui Abreu, Peter Zoeteweij, and Arjan J. C. van Gemund. 2006 · 2006
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Spectrum-based multiple fault localization
Rui Abreu, Peter Zoeteweij, and Arjan J. C. van Gemund. 2009 · 2009
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Automatically finding patches using genetic programming
Westley Weimer, ThanhVu Nguyen, Claire Le Goues, and Stephanie Forrest. 2009a · 2009
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Automatically finding patches using genetic programming
Westley Weimer, ThanhVu Nguyen, Claire Le Goues, and Stephanie Forrest. 2009b · 2009
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Localizing failure-inducing program edits based on spectrum information
Lingming Zhang, Miryung Kim, and Sarfraz Khurshid. 2011 · 2011
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A systematic study of automated program repair: Fixing 55 out of 105 bugs for $8 each
Claire Le Goues, Michael Dewey-Vogt, Stephanie Forrest, and Westley Weimer. 2012 · 2012
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Semfix: program repair via semantic analysis
Hoang Duong Thien Nguyen, Dawei Qi, Abhik Roychoudhury, and Satish Chandra. 2013 · 2013
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Injecting mechanical faults to localize developer faults for evolving software
Lingming Zhang, Lu Zhang, and Sarfraz Khurshid. 2013 · 2013
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Defects4j: a database of existing faults to enable controlled testing studies for java programs
René Just, Darioush Jalali, and Michael D. Ernst. 2014 · 2014
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Ask the mutants: Mutating faulty programs for fault localization
Seokhyeon Moon, Yunho Kim, Moonzoo Kim, and Shin Yoo. 2014 · 2014
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Staged program repair with condition synthesis
Fan Long and Martin C. Rinard. 2015 · 2015
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Counterexample-guided quantifier instantiation for synthesis in smt
Andrew Reynolds, Morgan Deters, Viktor Kuncak, Cesare Tinelli, and Clark Barrett. 2015 · 2015
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Practitioners’ expectations on automated fault localization
Pavneet Singh Kochhar, Xin Xia, David Lo, and Shanping Li. 2016 · 2016
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Angelix: scalable multiline program patch synthesis via symbolic analysis
Sergey Mechtaev, Jooyong Yi, and Abhik Roychoudhury. 2016 · 2016
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Transforming programs and tests in tandem for fault localization
Xia Li and Lingming Zhang. 2017 · 2017
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Quixbugs: a multi-lingual program repair benchmark set based on the quixey challenge
Derrick Lin, James Koppel, Angela Chen, and Armando Solar-Lezama. 2017 · 2017
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Codeflaws: a programming competition benchmark for evaluating automated program repair tools
Shin Hwei Tan, Jooyong Yi, Yulis, Sergey Mechtaev, and Abhik Roychoudhury. 2017 · 2017
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What do developers search for on the web?
Xin Xia, Lingfeng Bao, David Lo, Pavneet Singh Kochhar, Ahmed E. Hassan, and Zhenchang Xing. 2017 · 2017
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Overfitting in semantics-based automated program repair
Xuan-Bach Dinh Le, Ferdian Thung, David Lo, and Claire Le Goues. 2018 · 2018
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Automatic patch generation via learning from successful human patches
Fan Long. 2018 · 2018
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Deepfl: integrating multiple fault diagnosis dimensions for deep fault localization
Xia Li, Wei Li, Yuqun Zhang, and Lingming Zhang. 2019 · 2019
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Tbar: revisiting template-based automated program repair
Gemini: A family of highly capable multimodal models
Rohan Anil, Sebastian Borgeaud, Yonghui Wu, Jean-Baptiste Alayrac, Jiahui Yu, Radu Soricut, Johan Schalkwyk, Andrew M. Dai, Anja Hauth, Katie Millican, David Silver, Slav Petrov, Melvin Johnson, Ioannis Antonoglou, Julian Schrittwieser, Amelia Glaese, Jilin Chen, Emily Pitler, Timothy P. Lillicrap, Angeliki Lazaridou, Orhan Firat, James Molloy, Michael Isard, Paul Ronald Barham, Tom Hennigan, Benjamin Lee, Fabio Viola, Malcolm Reynolds, Yuanzhong Xu, Ryan Doherty, Eli Collins, Clemens Meyer, Eliza Rutherford, Erica Moreira, Kareem Ayoub, Megha Goel, George Tucker, Enrique Piqueras, Maxim Krikun, Iain Barr, Nikolay Savinov, Ivo Danihelka, Becca Roelofs, Anaïs White, Anders Andreassen, Tamara von Glehn, Lakshman Yagati, Mehran Kazemi, Lucas Gonzalez, Misha Khalman, Jakub Sygnowski, and et al. 2023 · 2023
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Self-collaboration code generation via chatgpt
Yihong Dong, Xue Jiang, Zhi Jin, and Ge Li. 2023 · 2023
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An empirical study on fine-tuning large language models of code for automated program repair
Kai Huang, Xiangxin Meng, Jian Zhang, Yang Liu, Wenjie Wang, Shuhao Li, and Yuqing Zhang. 2023 · 2023
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Kui Liu, Anil Koyuncu, Dongsun Kim, and Tegawendé F. Bissyandé. 2019 · 2019
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On the effectiveness of unified debugging: An extensive study on 16 program repair systems
Samuel Benton, Xia Li, Yiling Lou, and Lingming Zhang. 2020 · 2020
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Coconut: combining context-aware neural translation models using ensemble for program repair
Thibaud Lutellier, Hung Viet Pham, Lawrence Pang, Yitong Li, Moshi Wei, and Lin Tan. 2020 · 2020
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Evaluating large language models trained on code
Mark Chen, Jerry Tworek, Heewoo Jun, Qiming Yuan, Henrique Pondé de Oliveira Pinto, Jared Kaplan, Harrison Edwards, Yuri Burda, Nicholas Joseph, Greg Brockman, Alex Ray, Raul Puri, Gretchen Krueger, Michael Petrov, Heidy Khlaaf, Girish Sastry, Pamela Mishkin, Brooke Chan, Scott Gray, Nick Ryder, Mikhail Pavlov, Alethea Power, Lukasz Kaiser, Mohammad Bavarian, Clemens Winter, Philippe Tillet, Felipe Petroski Such, Dave Cummings, Matthias Plappert, Fotios Chantzis, Elizabeth Barnes, Ariel Herbert-Voss, William Hebgen Guss, Alex Nichol, Alex Paino, Nikolas Tezak, Jie Tang, Igor Babuschkin, Suchir Balaji, Shantanu Jain, William Saunders, Christopher Hesse, Andrew N. Carr, Jan Leike, Joshua Achiam, Vedant Misra, Evan Morikawa, Alec Radford, Matthew Knight, Miles Brundage, Mira Murati, Katie Mayer, Peter Welinder, Bob McGrew, Dario Amodei, Sam McCandlish, Ilya Sutskever, and Wojciech Zaremba. 2021 · 2021
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CURE: code-aware neural machine translation for automatic program repair
Nan Jiang, Thibaud Lutellier, and Lin Tan. 2021 · 2021
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Fault localization with code coverage representation learning
Yi Li, Shaohua Wang, and Tien N. Nguyen. 2021 · 2021
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Boosting coverage-based fault localization via graph-based representation learning
Yiling Lou, Qihao Zhu, Jinhao Dong, Xia Li, Zeyu Sun, Dan Hao, Lu Zhang, and Lingming Zhang. 2021 · 2021
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Baptiste Rozière, Jonas Gehring, Fabian Gloeckle, Sten Sootla, Itai Gat, Xiaoqing Ellen Tan, Yossi Adi, Jingyu Liu, Tal Remez, Jérémy Rapin, Artyom Kozhevnikov, Ivan Evtimov, Joanna Bitton, Manish Bhatt, Cristian Canton-Ferrer, Aaron Grattafiori, Wenhan Xiong, Alexandre Défossez, Jade Copet, Faisal Azhar, Hugo Touvron, Louis Martin, Nicolas Usunier, Thomas Scialom, and Gabriel Synnaeve. 2023 · 2023
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Evaluating the impact of experimental assumptions in automated fault localization
Ezekiel O. Soremekun, Lukas Kirschner, Marcel Böhme, and Mike Papadakis. 2023 · 2023
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Llama 2: Open foundation and fine-tuned chat models
Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, Dan Bikel, Lukas Blecher, Cristian Canton-Ferrer, Moya Chen, Guillem Cucurull, David Esiobu, Jude Fernandes, Jeremy Fu, Wenyin Fu, Brian Fuller, Cynthia Gao, Vedanuj Goswami, Naman Goyal, Anthony Hartshorn, Saghar Hosseini, Rui Hou, Hakan Inan, Marcin Kardas, Viktor Kerkez, Madian Khabsa, Isabel Kloumann, Artem Korenev, Punit Singh Koura, Marie-Anne Lachaux, Thibaut Lavril, Jenya Lee, Diana Liskovich, Yinghai Lu, Yuning Mao, Xavier Martinet, Todor Mihaylov, Pushkar Mishra, Igor Molybog, Yixin Nie, Andrew Poulton, Jeremy Reizenstein, Rashi Rungta, Kalyan Saladi, Alan Schelten, Ruan Silva, Eric Michael Smith, Ranjan Subramanian, Xiaoqing Ellen Tan, Binh Tang, Ross Taylor, Adina Williams, Jian Xiang Kuan, Puxin Xu, Zheng Yan, Iliyan Zarov, Yuchen Zhang, Angela Fan, Melanie Kambadur, Sharan Narang, Aurélien Rodriguez, Robert Stojnic, Sergey Edunov, and Thomas Scialom. 2023 · 2023
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Copiloting the copilots: Fusing large language models with completion engines for automated program repair
Yuxiang Wei, Chunqiu Steven Xia, and Lingming Zhang. 2023 · 2023
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Automated program repair in the era of large pre-trained language models
Chunqiu Steven Xia, Yuxiang Wei, and Lingming Zhang. 2023 · 2023
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Introducing the next generation of claude
Anthropic. 2024 · 2024
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Deepseek-coder: When the large language model meets programming - the rise of code intelligence
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Metagpt: Meta programming for A multi-agent collaborative framework
Sirui Hong, Mingchen Zhuge, Jonathan Chen, Xiawu Zheng, Yuheng Cheng, Jinlin Wang, Ceyao Zhang, Zili Wang, Steven Ka Shing Yau, Zijuan Lin, Liyang Zhou, Chenyu Ran, Lingfeng Xiao, Chenglin Wu, and Jürgen Schmidhuber. 2024 · 2024
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Agentcoder: Multi-agent-based code generation with iterative testing and optimisation
Dong Huang, Jie M. Zhang, Michael Luck, Qingwen Bu, Yuhao Qing, and Heming Cui. 2024 · 2024
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Mapcoder: Multi-agent code generation for competitive problem solving
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Chatdev: Communicative agents for software development
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Debugbench: Evaluating debugging capability of large language models
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Condefects: A complementary dataset to address the data leakage concern for llm-based fault localization and program repair
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Automated program repair via conversation: Fixing 162 out of 337 bugs for $0.42 each using chatgpt
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Large language models for test-free fault localization
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