Fetching the paper…
Reading the bibliography…
Large Language Model (LLM) systems have been at the forefront of applied Artificial Intelligence (AI) research in a multitude of domains.
A probabilistic model of information retrieval: development and comparative experiments: Part 2
K. S. Jones, S. Walker, and S. E. Robertson · 2000
Earlier work this paper cites.
Bugl – a cross-language dataset for bug localization, 2020
S. Muvva, A. E. Rao, and S. Chimalakonda · 2004
Earlier work this paper cites.
Idf revisited: a simple new derivation within the robertson-spärck jones probabilistic model
L. Lee · 2007
Earlier work this paper cites.
Lower-bounding term frequency normalization
Y. Lv and C. Zhai · 2011
Earlier work this paper cites.
On the naturalness of software
A. Hindle, E. T. Barr, Z. Su, M. Gabel, and P. Devanbu · 2012
Earlier work this paper cites.
Where should the bugs be fixed? more accurate information retrieval-based bug localization based on bug reports
J. Zhou, H. Zhang, and D. Lo · 2012
Earlier work this paper cites.
Improving bug localization using structured information retrieval
R. K. Saha, M. Lease, S. Khurshid, and D. E. Perry · 2013
Earlier work this paper cites.
Combining deep learning with information retrieval to localize buggy files for bug reports (n)
A. N. Lam, A. T. Nguyen, H. A. Nguyen, and T. N. Nguyen · 2015
Earlier work this paper cites.
Machine learning or information retrieval techniques for bug triaging: Which is better?
A. Goyal and N. Sardana · 2017
Earlier work this paper cites.
Bug localization with combination of deep learning and information retrieval
A. N. Lam, A. T. Nguyen, H. A. Nguyen, and T. N. Nguyen · 2017
Earlier work this paper cites.
Today was a good day: The daily life of software developers
A. N. Meyer, E. T. Barr, C. Bird, and T. Zimmermann · 2019
Earlier work this paper cites.
On usefulness of the deep-learning-based bug localization models to practitioners
S. Polisetty, A. Miranskyy, and A. Başar · 2019
Earlier work this paper cites.
Pythia: Ai-assisted code completion system
A. Svyatkovskiy, Y. Zhao, S. Fu, and N. Sundaresan · 2019
Earlier work this paper cites.
Codebert: A pre-trained model for programming and natural languages, 2020
Z. Feng, D. Guo, D. Tang, N. Duan, X. Feng, M. Gong, L. Shou, B. Qin, T. Liu, D. Jiang, and M. Zhou · 2020
Earlier work this paper cites.
Program synthesis with large language models
J. Austin, A. Odena, M. Nye, M. Bosma, H. Michalewski, D. Dohan, E. Jiang, C. Cai, M. Terry, Q. Le, et al · 2021
Earlier work this paper cites.
Evaluating large language models trained on code, 2021
M. Chen, J. Tworek, H. Jun, Q. Yuan, H. P. de Oliveira Pinto, J. Kaplan, H. Edwards, Y. Burda, N. Joseph, G. Brockman, A. Ray, R. Puri, G. Krueger, M. Petrov, H. Khlaaf, G. Sastry, P. Mishkin, B. Chan, S. Gray, N. Ryder, M. Pavlov, A. Power, L. Kaiser, M. Bavarian, C. Winter, P. Tillet, F. P. Such, D. Cummings, M. Plappert, F. Chantzis, E. Barnes, A. Herbert-Voss, W. H. Guss, A. Nichol, A. Paino, N. Tezak, J. Tang, I. Babuschkin, S. Balaji, S. Jain, W. Saunders, C. Hesse, A. N. Carr, J. Leike, J. Achiam, V. Misra, E. Morikawa, A. Radford, M. Knight, M. Brundage, M. Murati, K. Mayer, P. Welinder, B. McGrew, D. Amodei, S. McCandlish, I. Sutskever, and W. Zaremba · 2021
Earlier work this paper cites.
An extensive study on smell-aware bug localization
A. Takahashi, N. Sae-Lim, S. Hayashi, and M. Saeki · 2021
Earlier work this paper cites.
Codet5: Identifier-aware unified pre-trained encoder-decoder models for code understanding and generation, 2021
Y. Wang, W. Wang, S. Joty, and S. C. H. Hoi · 2021
Earlier work this paper cites.
Few-shot training llms for project-specific code-summarization
T. Ahmed and P. Devanbu · 2022
Earlier work this paper cites.
Ai for automated code updates
S. Alamir, P. Babkin, N. Navarro, and S. Shah · 2022
Cited alongside, same era.
A review on software bug localization techniques using a motivational example
A. M. Mohsen, H. Hassan, R. Moawad, and S. H. Makady · 2022
Cited alongside, same era.
A systematic literature review on the use of deep learning in software engineering research
C. Watson, N. Cooper, D. N. Palacio, K. Moran, and D. Poshyvanyk · 2022
Cited alongside, same era.
Large language models for software engineering: Survey and open problems
A. Fan, B. Gokkaya, M. Harman, M. Lyubarskiy, S. Sengupta, S. Yoo, and J. M. Zhang · 2023
Cited alongside, same era.
Self-planning code generation with large language models
X. Jiang, Y. Dong, L. Wang, F. Zheng, Q. Shang, G. Li, Z. Jin, and W. Jiao · 2023
Cited alongside, same era.
Swe-bench: Can language models resolve real-world github issues?
Software vulnerability and functionality assessment using llms
R. I. T. Jensen, V. Tawosi, and S. Alamir · 2024
Later among the works it cites.
A survey on large language models for code generation
J. Jiang, F. Wang, J. Shen, S. Kim, and S. Kim · 2024
Later among the works it cites.
Long-context llms struggle with long in-context learning
T. Li, G. Zhang, Q. D. Do, X. Yue, and W. Chen · 2024
Later among the works it cites.
Llm-powered test case generation for detecting tricky bugs
K. Liu, Y. Liu, Z. Chen, J. M. Zhang, Y. Han, Y. Ma, G. Li, and G. Huang · 2024
Later among the works it cites.
Starcoder 2 and the stack v2: The next generation
A. Lozhkov, R. Li, L. B. Allal, F. Cassano, J. Lamy-Poirier, N. Tazi, A. Tang, D. Pykhtar, J. Liu, Y. Wei, et al · 2024
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
C. E. Jimenez, J. Yang, A. Wettig, S. Yao, K. Pei, O. Press, and K. Narasimhan · 2023
Cited alongside, same era.
Inferfix: End-to-end program repair with llms
M. Jin, S. Shahriar, M. Tufano, X. Shi, S. Lu, N. Sundaresan, and A. Svyatkovskiy · 2023
Cited alongside, same era.
Starcoder: may the source be with you!, 2023
R. Li, L. B. Allal, Y. Zi, N. Muennighoff, D. Kocetkov, C. Mou, M. Marone, C. Akiki, J. Li, J. Chim, Q. Liu, E. Zheltonozhskii, T. Y. Zhuo, T. Wang, O. Dehaene, M. Davaadorj, J. Lamy-Poirier, J. Monteiro, O. Shliazhko, N. Gontier, N. Meade, A. Zebaze, M.-H. Yee, L. K. Umapathi, J. Zhu, B. Lipkin, M. Oblokulov, Z. Wang, R. Murthy, J. Stillerman, S. S. Patel, D. Abulkhanov, M. Zocca, M. Dey, Z. Zhang, N. Fahmy, U. Bhattacharyya, W. Yu, S. Singh, S. Luccioni, P. Villegas, M. Kunakov, F. Zhdanov, M. Romero, T. Lee, N. Timor, J. Ding, C. Schlesinger, H. Schoelkopf, J. Ebert, T. Dao, M. Mishra, A. Gu, J. Robinson, C. J. Anderson, B. Dolan-Gavitt, D. Contractor, S. Reddy, D. Fried, D. Bahdanau, Y. Jernite, C. M. Ferrandis, S. Hughes, T. Wolf, A. Guha, L. von Werra, and H. de Vries · 2023
Cited alongside, same era.
Memgpt: Towards llms as operating systems
C. Packer, V. Fang, S. G. Patil, K. Lin, S. Wooders, and J. E. Gonzalez · 2023
Cited alongside, same era.
Generative agents: Interactive simulacra of human behavior
J. S. Park, J. O’Brien, C. J. Cai, M. R. Morris, P. Liang, and M. S. Bernstein · 2023
Cited alongside, same era.
Search-based optimisation of llm learning shots for story point estimation
V. Tawosi, S. Alamir, and X. Liu · 2023
Cited alongside, same era.
Automated program repair in the era of large pre-trained language models
C. S. Xia, Y. Wei, and L. Zhang · 2023
Cited alongside, same era.
Later among the works it cites.
Ragfix: Enhancing llm code repair using rag and stack overflow posts
E. Mansur, J. Chen, M. A. Raza, and M. Wardat · 2024
Later among the works it cites.
Using an llm to help with code understanding
D. Nam, A. Macvean, V. Hellendoorn, B. Vasilescu, and B. Myers · 2024
Later among the works it cites.
OpenAI Models - GPT-4o, 2024
OpenAI · 2024
Later among the works it cites.
Lost in translation: A study of bugs introduced by large language models while translating code
R. Pan, A. R. Ibrahimzada, R. Krishna, D. Sankar, L. P. Wassi, M. Merler, B. Sobolev, R. Pavuluri, S. Sinha, and R. Jabbarvand · 2024
Later among the works it cites.
Programming-by-demonstration for long-horizon robot tasks
N. Patton, K. Rahmani, M. Missula, J. Biswas, and I. Dillig · 2024
Later among the works it cites.
Code llama: Open foundation models for code, 2024
B. Rozière, J. Gehring, F. Gloeckle, S. Sootla, I. Gat, X. E. Tan, Y. Adi, J. Liu, R. Sauvestre, T. Remez, J. Rapin, A. Kozhevnikov, I. Evtimov, J. Bitton, M. Bhatt, C. C. Ferrer, A. Grattafiori, W. Xiong, A. Défossez, J. Copet, F. Azhar, H. Touvron, L. Martin, N. Usunier, T. Scialom, and G. Synnaeve · 2024
Later among the works it cites.
Code-aware prompting: A study of coverage-guided test generation in regression setting using llm
G. Ryan, S. Jain, M. Shang, S. Wang, X. Ma, M. K. Ramanathan, and B. Ray · 2024
Later among the works it cites.
Source code summarization in the era of large language models
W. Sun, Y. Miao, Y. Li, H. Zhang, C. Fang, Y. Liu, G. Deng, Y. Liu, and Z. Chen · 2024
Later among the works it cites.
How and why llms use deprecated apis in code completion? an empirical study
C. Wang, K. Huang, J. Zhang, Y. Feng, L. Zhang, Y. Liu, and X. Peng · 2024
Later among the works it cites.
Agentless: Demystifying llm-based software engineering agents
C. S. Xia, Y. Deng, S. Dunn, and L. Zhang · 2024
Later among the works it cites.
Swe-agent: Agent-computer interfaces enable automated software engineering
J. Yang, C. E. Jimenez, A. Wettig, K. Lieret, S. Yao, K. Narasimhan, and O. Press · 2024
Later among the works it cites.
Autocoderover: Autonomous program improvement
Y. Zhang, H. Ruan, Z. Fan, and A. Roychoudhury · 2024
Later among the works it cites.
Accountability in code review: The role of intrinsic drivers and the impact of llms
A. Alami, V. V. Jensen, and N. A. Ernst · 2025
Closest in time.