Fetching the paper…
Reading the bibliography…
Current approaches to program synthesis with Large Language Models (LLMs) exhibit a "near miss syndrome": they tend to generate programs that semantically resemble the correct answer (as measured by text similarity metrics or human evaluation), but achieve a low or even zero accuracy as measured by unit tests due to small imperfections, such as the wrong input or output format.
“CodeSearchNet Challenge: Evaluating the State of Semantic Code Search”
Hamel Husain, Ho-Hsiang Wu, Tiferet Gazit, Miltiadis Allamanis and Marc Brockschmidt · 1909
Earlier work this paper cites.
“Toward Automatic Program Synthesis”
Zohar Manna and Richard. Waldinger · 1971
Earlier work this paper cites.
“Programming by Example”, 1984
Daniel Halbert · 1984
Earlier work this paper cites.
“Fundamentals of Deductive Program Synthesis”
Z. Manna and R. Waldinger · 1992
Earlier work this paper cites.
“Genetic Programming II”
John Koza · 1994
Earlier work this paper cites.
“Optimal Repair–Replace Strategies for a Warranted Product”
Nat Jack and Frank Van der Duyn Schouten · 2000
Earlier work this paper cites.
“Nine Worlds of Seid-magic: Ecstasy and Neo-shamanism in North European Paganism”
Jenny Blain · 2002
Earlier work this paper cites.
“CodeBLEU: A Method for Automatic Evaluation of Code Synthesis”
Shuo Ren et al · 2009
Earlier work this paper cites.
“Artificial Intelligence a Modern Approach”
Stuart Russell · 2010
Earlier work this paper cites.
“Syntax-Guided Synthesis”
Rajeev Alur et al · 2015
Earlier work this paper cites.
“FlashMeta: A Framework for Inductive Program Synthesis”
Oleksandr Polozov and Sumit Gulwani · 2015
Earlier work this paper cites.
“General Program Synthesis Benchmark Suite”
Thomas Helmuth and Lee Spector · 2015
Earlier work this paper cites.
“A Review-Based Comparative Study of Bad Smell Detection Tools”
Eduardo Fernandes, Johnatan Oliveira, Gustavo Vale, Thanis Paiva and Eduardo Figueiredo · 2016
Earlier work this paper cites.
“Programming by Examples (and Its Applications in Data Wrangling)”, 2016, pp. 22
Sumit Gulwani · 2016
Earlier work this paper cites.
“Attention Is All You Need”
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan Gomez, Łukasz Kaiser and Illia Polosukhin · 2017
Earlier work this paper cites.
“QuixBugs: A Multi-Lingual Program Repair Benchmark Set Based on the Quixey Challenge”
Derrick Lin, James Koppel, Angela Chen and Armando Solar-Lezama · 2017
Earlier work this paper cites.
“A Survey of Machine Learning for Big Code and Naturalness”
Miltiadis Allamanis, Earl. Barr, Premkumar Devanbu and Charles Sutton · 2018
Earlier work this paper cites.
“Genetic Improvement of Software: A Comprehensive Survey”
Justyna Petke, Saemundur. Haraldsson, Mark Harman, William. Langdon, David. White and John. Woodward · 2018
Earlier work this paper cites.
“Mapping Language to Code in Programmatic Context”
Srinivasan Iyer, Ioannis Konstas, Alvin Cheung and Luke Zettlemoyer · 2018
Cited alongside, same era.
“NAPS: Natural Program Synthesis Dataset”
Maksym Zavershynskyi, Alex Skidanov and Illia Polosukhin · 2018
Cited alongside, same era.
“A Survey of Genetic Programming and Its Applications”
Milad Ahvanooey, Qianmu Li, Ming Wu and Shuo Wang · 2019
Cited alongside, same era.
“Automated Program Repair”
Claire Le, Michael Pradel and Abhik Roychoudhury · 2019
Cited alongside, same era.
“A Survey of Deep Learning Techniques for Autonomous Driving”
Sorin Grigorescu, Bogdan Trasnea, Tiberiu Cocias and Gigel Macesanu · 2020
Cited alongside, same era.
“Competition-Level Code Generation with AlphaCode”
Yujia Li et al · 2022
Later among the works it cites.
“Neurosymbolic Repair for Low-Code Formula Languages”
Rohan Bavishi, Harshit Joshi, José Cambronero, Anna Fariha, Sumit Gulwani, Vu Le, Ivan Radicek and Ashish Tiwari · 2022
Later among the works it cites.
“Deep Learning Based Vulnerability Detection: Are We There Yet?”
Saikat Chakraborty, Rahul Krishna, Yangruibo Ding and Baishakhi Ray · 2022
Later among the works it cites.
“Training Language Models to Follow Instructions with Human Feedback”
Long Ouyang et al · 2022
Later among the works it cites.
“Repair Is Nearly Generation: Multilingual Program Repair with LLMs”
Harshit Joshi, José Cambronero, Sumit Gulwani, Vu Le, Ivan Radicek and Gust Verbruggen · 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…
Mauricio Marcano, Sergio Díaz, Joshué Pérez and Eloy Irigoyen · 2020
Cited alongside, same era.
“Unsupervised Translation of Programming Languages”
Baptiste Roziere, Marie-Anne Lachaux, Guillaume Lample and Lowik Chanussot · 2020
Cited alongside, same era.
“Synthesize, Execute and Debug: Learning to Repair for Neural Program Synthesis”
Kavi Gupta, Peter Christensen, Xinyun Chen and Dawn Song · 2020
Cited alongside, same era.
“A Survey of On-Device Machine Learning: An Algorithms and Learning Theory Perspective”
Sauptik Dhar, Junyao Guo, Jiayi Liu, Samarth Tripathi, Unmesh Kurup and Mohak Shah · 2021
Cited alongside, same era.
“Evaluating Large Language Models Trained on Code”
Mark Chen et al · 2021
Cited alongside, same era.
“Latent Execution for Neural Program Synthesis Beyond Domain-Specific Languages”
Xinyun Chen, Dawn Song and Yuandong Tian · 2021
Cited alongside, same era.
“BF++: A Language for General-Purpose Program Synthesis”
Vadim Liventsev, Aki Härmä and Milan Petković · 2021
Cited alongside, same era.
Disha Shrivastava, Hugo Larochelle and Daniel Tarlow · 2022
Later among the works it cites.
Qing Huang, Zhiqiang Yuan, Zhenchang Xing, Xiwei Xu, Liming Zhu and Qinghua Lu · 2022
Later among the works it cites.
“Can OpenAI’s Codex Fix Bugs?: An Evaluation on QuixBugs”
Julian Prenner, Hlib Babii and Romain Robbes · 2022
Later among the works it cites.
“Less Is More: Summary of Long Instructions Is Better for Program Synthesis”
Kirby Kuznia, Swaroop Mishra, Mihir Parmar and Chitta Baral · 2022
Later among the works it cites.
“Applying Genetic Programming to PSB2: The next Generation Program Synthesis Benchmark Suite”
Thomas Helmuth and Peter Kelly · 2022
Later among the works it cites.
“Problem-Solving Benefits of Down-Sampled Lexicase Selection”
Thomas Helmuth and Lee Spector · 2022
Later among the works it cites.
“Choose Your Programming Copilot: A Comparison of the Program Synthesis Performance of Github Copilot and Genetic Programming”
Dominik Sobania, Martin Briesch and Franz Rothlauf · 2022
Later among the works it cites.
“Systematic Literature Review: XAI and Clinical Decision Support”
Thomas. Connolly, Mario Soflano and Petros Papadopoulos · 2023
Closest in time.
“CrossCodeBench: Benchmarking Cross-Task Generalization of Source Code Models”
Changan Niu, Chuanyi Li, Vincent Ng and Bin Luo · 2023
Closest in time.
“Automated Repair of Programs from Large Language Models”
Zhiyu Fan, Xiang Gao, Martin Mirchev, Abhik Roychoudhury and Shin Tan · 2023
Closest in time.
“Fixing Hardware Security Bugs with Large Language Models”
Baleegh Ahmad, Shailja Thakur, Benjamin Tan, Ramesh Karri and Hammond Pearce · 2023
Closest in time.
“An Analysis of the Automatic Bug Fixing Performance of ChatGPT”
Dominik Sobania, Martin Briesch, Carol Hanna and Justyna Petke · 2023
Closest in time.