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Software optimization refines programs for resource efficiency while preserving functionality.
“Language models are few-shot learners”
Tom Brown et al · 1901
Earlier work this paper cites.
“Machine learning in compiler optimization”
Zheng Wang and Michael O’Boyle · 1901
Earlier work this paper cites.
“Pattern matching: The gestalt approach”
John Ratcliff and David Metzener · 1988
Earlier work this paper cites.
“Toward understanding the rhetoric of small source code changes”
Ranjith Purushothaman and Dewayne Perry · 2005
Earlier work this paper cites.
“Using machine learning to focus iterative optimization”
Felix Agakov et al · 2006
Earlier work this paper cites.
“What’s a typical commit? a characterization of open source software repositories”
Abdulkareem Alali, Huzefa Kagdi and Jonathan Maletic · 2008
Earlier work this paper cites.
“Small patches get in!”
Peter Weißgerber, Daniel Neu and Stephan Diehl · 2008
Earlier work this paper cites.
“Using machine learning to partition streaming programs”
Zheng Wang and Michael O’boyle · 2013
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“Stochastic superoptimization”
Eric Schkufza, Rahul Sharma and Alex Aiken · 2013
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“Sequence to sequence learning with neural networks”
Ilya Sutskever, Oriol Vinyals and Quoc Le · 2014
Earlier work this paper cites.
“On the properties of neural machine translation: Encoder-decoder approaches”
Kyunghyun Cho, Bart Vanënboer, Dzmitry Bahdanau and Yoshua Bengio · 2014
Earlier work this paper cites.
“Post-compiler software optimization for reducing energy”
Eric Schulte et al · 2014
Earlier work this paper cites.
“Tricorder: Building a program analysis ecosystem”
Caitlin Sadowski et al · 2015
Earlier work this paper cites.
“Google’s neural machine translation system: Bridging the gap between human and machine translation”
Yonghui Wu et al · 2016
Earlier work this paper cites.
“Finding resilience-friendly compiler optimizations using meta-heuristic search techniques”
Nithya Narayanamurthy, Karthik Pattabiraman and Matei Ripeanu · 2016
Earlier work this paper cites.
“Learning to superoptimize programs”
Rudy Bunel et al · 2016
Earlier work this paper cites.
“Attention is all you need”
Ashish Vaswani et al · 2017
Earlier work this paper cites.
“End-to-end deep learning of optimization heuristics”
Chris Cummins, Pavlos Petoumenos, Zheng Wang and Hugh Leather · 2017
Earlier work this paper cites.
“Sound loop superoptimization for google native client”
Berkeley Churchill, Rahul Sharma, JF Bastien and Alex Aiken · 2017
Earlier work this paper cites.
“Genetic improvement of software: a comprehensive survey”
Justyna Petke et al · 2017
Earlier work this paper cites.
“Improving spark application throughput via memory aware task co-location: A mixture of experts approach”
Vicent Marco, Ben Taylor, Barry Porter and Zheng Wang · 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
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“Improving language understanding by generative pre-training”
Alec Radford, Karthik Narasimhan, Tim Salimans and Ilya Sutskever · 2018
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“Deep code search”
Xiaodong Gu, Hongyu Zhang and Sunghun Kim · 2018
Cited alongside, same era.
“Deep code comment generation”
Xing Hu et al · 2018
Cited alongside, same era.
“How training data affect the accuracy and robustness of neural networks for image classification”, 2018
“A Deep Learning Based Cost Model for Automatic Code Optimization”
Riyadh Baghdadi et al · 2021
Later among the works it cites.
“DeepDev-PERF: a deep learning-based approach for improving software performance”
Spandan Garg et al · 2022
Later among the works it cites.
“Bloom: A 176b-parameter open-access multilingual language model”
Teven Scao et al · 2022
Later among the works it cites.
“Palm: Scaling language modeling with pathways”
Aakanksha Chowdhery et al · 2022
Later among the works it cites.
“Introducing ChatGPT”, 2022
OpenAI · 2022
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“Training language models to follow instructions with human feedback”
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Suhua Lei, Huan Zhang, Ke Wang and Zhendong Su · 2018
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“A survey on compiler autotuning using machine learning”
Amir Ashouri et al · 2018
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“Learning to optimize tensor programs”
Tianqi Chen et al · 2018
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Mike Lewis et al · 2019
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“Unsupervised pre-training of a deep LSTM-based stacked autoencoder for multivariate time series forecasting problems”
Alaa Sagheer and Mostafa Kotb · 2019
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“Exploring the limits of transfer learning with a unified text-to-text transformer”
Colin Raffel et al · 2020
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“Codebert: A pre-trained model for programming and natural languages”
Zhangyin Feng et al · 2020
Cited alongside, same era.
Long Ouyang et al · 2022
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“Exploring length generalization in large language models”
Cem Anil et al · 2022
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“Learning to Improve Code Efficiency”
Binghong Chen et al · 2022
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“Is ChatGPT the Ultimate Programming Assistant–How far is it?”
Haoye Tian et al · 2023
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“Improving ChatGPT Prompt for Code Generation”
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“AI-assisted coding: Experiments with GPT-4”
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“Llama: Open and efficient foundation language models”
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“Use chat gpt to solve programming bugs”
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“CodeBERTScore: Evaluating Code Generation with Pretrained Models of Code”
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