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An Exploratory Literature Study on Sharing and Energy Use of Language Models for Source Code (2307.02443v1)

Published 5 Jul 2023 in cs.SE, cs.AI, cs.CL, cs.LG, and cs.NE

Abstract: LLMs trained on source code can support a variety of software development tasks, such as code recommendation and program repair. Large amounts of data for training such models benefit the models' performance. However, the size of the data and models results in long training times and high energy consumption. While publishing source code allows for replicability, users need to repeat the expensive training process if models are not shared. The main goal of the study is to investigate if publications that trained LLMs for software engineering (SE) tasks share source code and trained artifacts. The second goal is to analyze the transparency on training energy usage. We perform a snowballing-based literature search to find publications on LLMs for source code, and analyze their reusability from a sustainability standpoint. From 494 unique publications, we identified 293 relevant publications that use LLMs to address code-related tasks. Among them, 27% (79 out of 293) make artifacts available for reuse. This can be in the form of tools or IDE plugins designed for specific tasks or task-agnostic models that can be fine-tuned for a variety of downstream tasks. Moreover, we collect insights on the hardware used for model training, as well as training time, which together determine the energy consumption of the development process. We find that there are deficiencies in the sharing of information and artifacts for current studies on source code models for software engineering tasks, with 40% of the surveyed papers not sharing source code or trained artifacts. We recommend the sharing of source code as well as trained artifacts, to enable sustainable reproducibility. Moreover, comprehensive information on training times and hardware configurations should be shared for transparency on a model's carbon footprint.

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References (127)
  1. “The FAIR Guiding Principles for Scientific Data Management and Stewardship” In Scientific Data 3.1, 2016, pp. 160018 DOI: 10/bdd4
  2. “Towards FAIR Principles for Research Software” In Data Science 3.1, 2020, pp. 37–59 DOI: 10/gg66q6
  3. “VideoBERT: A Joint Model for Video and Language Representation Learning” In Int’l Conf. Comp. Vision, 2019, pp. 7464–7473
  4. “Language Models Are Few-Shot Learners” In Int’l Conf. Neural Information Processing Sys., NIPS’20 Red Hook, NY, USA: Curran, 2020, pp. 1877–1901
  5. Emma Strubell, Ananya Ganesh and Andrew McCallum “Energy and Policy Considerations for Deep Learning in NLP” In Meeting of the Association for Computational Linguistics, 2019, pp. 3645–3650 DOI: 10/ggbgzx
  6. “Green AI” In Comm. ACM 63.12, 2020, pp. 54–63 DOI: 10/ghvhs3
  7. “Green AI: Do Deep Learning Frameworks Have Different Costs?” In Int’l Conf. Softw. Eng., 2022, pp. 1082–1094 DOI: 10/gq4wm2
  8. Sindhu Tipirneni, Ming Zhu and Chandan K. Reddy “StructCoder: Structure-Aware Transformer for Code Generation” arXiv, 2022 DOI: 10.48550/arXiv.2206.05239
  9. “Distributed Representations of Words and Phrases and Their Compositionality” In Int’l Conf. Neural Information Processing Sys. Lake Tahoe, Nevada: Curran, 2013, pp. 3111–3119
  10. “Evaluating Large Language Models Trained on Code” arXiv, 2021 DOI: 10.48550/arXiv.2107.03374
  11. “CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and Generation” In Neural Information Processing Sys. Track on Datasets and Benchmarks, 2021
  12. “HULK: An Energy Efficiency Benchmark Platform for Responsible Natural Language Processing” arXiv, 2020 DOI: 10.48550/arXiv.2002.05829
  13. “BLOOM: A 176B-Parameter Open-Access Multilingual Language Model” arXiv, 2022 DOI: 10.48550/arXiv.2211.05100
  14. Daniel S Katz, Fotis E Psomopoulos and Leyla Jael Castro “Working towards Understanding the Role of FAIR for Machine Learning.” In Ws. Data and Research Objects Management for Linked Open Science, 2021, pp. 1–6
  15. “On the Reproducibility and Replicability of Deep Learning in Software Engineering” In ACM Trans. Softw. Eng. and Methodology 31.1, 2021, pp. 15:1–15:46 DOI: 10/gq4s96
  16. “Towards a Software Sustainability-Quality Model: Insights from a Multi-Case Study” In Int’l Conf. Research Challenges in Information Science, 2019 DOI: 10/gsdpcb
  17. “Framing Sustainability as a Property of Software Quality” In Comm. ACM 58.10, 2015, pp. 70–78 DOI: 10/f3ppcb
  18. “A Software Sustainability-Quality Model”, 2018
  19. Bill Tomlinson “Greening through IT: Information Technology for Environmental Sustainability”, 2010 DOI: 10.7551/mitpress/8261.001.0001
  20. “Enhancing Software Engineering Processes towards Sustainable Software Product Design” In Integration of Environmental Information in Europe Shaker Verlag, 2010
  21. Abram Hindle “Green Mining: A Methodology of Relating Software Change and Configuration to Power Consumption” In Emp. Softw. Eng. 20.2 Springer, 2015, pp. 374–409 DOI: 10/f7bbjj
  22. “Empirical Evaluation of the Energy Impact of Refactoring Code Smells” In Int’l Conf. ICT for Sustainability 52, 2018, pp. 365–383 DOI: 10/grbc94
  23. “The GREENSOFT Model: A Reference Model for Green and Sustainable Software and Its Engineering” In Sustainable Computing: Informatics and Systems 1.4, 2011, pp. 294–304 DOI: 10/b9scx4
  24. Matias Martinez, Silverio Martínez-Fernández and Xavier Franch “Energy Consumption of Automated Program Repair” arXiv, 2022 DOI: 10.48550/arXiv.2211.12104
  25. “Data-Centric Green AI: An Exploratory Empirical Study” In Int’l Conf. ICT for Sustainability, 2022, pp. 1–11
  26. “Estimation of Energy Consumption in Machine Learning” In J. Parallel and Distributed Computing 134, 2019, pp. 75–88 DOI: 10/ggbx92
  27. “Evaluating the Energy Efficiency of Deep Convolutional Neural Networks on CPUs and GPUs” In IEEE Int’l Conf.s on Big Data and Cloud Computing (BDCloud), Social Computing and Networking (SocialCom), Sustainable Computing and Communications Atlanta, GA, USA: IEEE, 2016, pp. 477–484 DOI: 10/grdjz2
  28. “Towards the Systematic Reporting of the Energy and Carbon Footprints of Machine Learning” In J. Machine Learning Research 21.1 JMLR, 2022, pp. 248:10039–248:10081
  29. Maria Gutierrez, Ma Angeles Moraga and Felix Garcia “Analysing the Energy Impact of Different Optimisations for Machine Learning Models” In Int’l Conf. ICT for Sustainability Plovdiv, Bulgaria: IEEE, 2022, pp. 46–52 DOI: 10/grb825
  30. Eva Garcia-Martin, Niklas Lavesson and Håkan Grahn “Identification of Energy Hotspots: A Case Study of the Very Fast Decision Tree” In Green, Pervasive, and Cloud Computing 10232 Cham: Springer International Publishing, 2017, pp. 267–281 DOI: 10.1007/978-3-319-57186-7˙21
  31. Roberto Verdecchia, June Sallou and Luís Cruz “A Systematic Review of Green AI” arXiv, 2023 DOI: 10.48550/arXiv.2301.11047
  32. “Systematic Literature Studies: Database Searches vs. Backward Snowballing” In Int’l Symp. Empirical Softw. Eng. and Measurement New York, New York, USA: ACM, 2012, pp. 29–38 DOI: 10/f2324f
  33. Claes Wohlin “Guidelines for Snowballing in Systematic Literature Studies and a Replication in Software Engineering” In Int’l Conf. Evaluation and Assessment in Softw. Eng. ACM, 2014, pp. 1–10 DOI: 10/f22cpf
  34. Kai Petersen, Sairam Vakkalanka and Ludwik Kuzniarz “Guidelines for Conducting Systematic Mapping Studies in Software Engineering: An Update” In Information and Softw. Technology 64, 2015, pp. 1–18 DOI: 10/f3pxzb
  35. “A Literature Study of Embeddings on Source Code” arXiv, 2019 DOI: 10.48550/arXiv.1904.03061
  36. “A Survey on Machine Learning Techniques for Source Code Analysis” arXiv, 2021 DOI: 10.48550/arXiv.2110.09610
  37. “A Systematic Literature Review on the Use of Deep Learning in Software Engineering Research” In ACM Trans. Softw. Eng. and Methodology 31.2 ACM, 2022, pp. 32:1–32:58 DOI: 10/gqcrq6
  38. “Deep Learning Meets Software Engineering: A Survey on Pre-Trained Models of Source Code” arXiv, 2022 DOI: 10.48550/arXiv.2205.11739
  39. “DeepSim: Deep Learning Code Functional Similarity” In ACM J. Meeting Eur. Softw. Eng. Conf. and Symp. Found. Softw. Eng. Lake Buena Vista FL USA: ACM, 2018, pp. 141–151 DOI: 10/ghhrrs
  40. “Functional Code Clone Detection with Syntax and Semantics Fusion Learning” In ACM SIGSOFT Int’l Symp. Softw. Testing and Analysis Virtual Event USA: ACM, 2020, pp. 516–527 DOI: 10/ghhrrp
  41. Daniel DeFreez, Aditya V. Thakur and Cindy Rubio-González “Path-Based Function Embedding and Its Application to Specification Mining” arXiv, 2018 DOI: 10.48550/arXiv.1802.07779
  42. “Neural Detection of Semantic Code Clones Via Tree-Based Convolution” In IEEE/ACM 27th Int’l Conf. Program Comprehension Montreal, QC, Canada: IEEE, 2019, pp. 70–80 DOI: 10/gq4wj2
  43. Shaked Brody, Uri Alon and Eran Yahav “A Structural Model for Contextual Code Changes” arXiv, 2020 DOI: 10.48550/arXiv.2005.13209
  44. “Structural Language Models of Code” In Int’l Conf. Machine Learning PMLR, 2020, pp. 245–256
  45. “A Retrieve-and-Edit Framework for Predicting Structured Outputs” In Int’l Conf. Neural Information Processing Sys., NIPS’18 Red Hook, NY, USA: Curran, 2018, pp. 10073–10083
  46. “CODIT: Code Editing With Tree-Based Neural Models” In IEEE Trans. Softw. Eng. 48.4, 2022, pp. 1385–1399 DOI: 10/gnxdg4
  47. “IntelliCode Compose: Code Generation Using Transformer” arXiv, 2020 DOI: 10.48550/arXiv.2005.08025
  48. “Pythia: AI-assisted Code Completion System” In ACM SIGKDD Int’l Conf. Knowledge Discovery & Data Mining, KDD ’19 New York, NY, USA: ACM, 2019, pp. 2727–2735 DOI: 10/gf7nbt
  49. “Neural Sketch Learning for Conditional Program Generation” arXiv, 2018 DOI: 10.48550/arXiv.1703.05698
  50. “Incorporating External Knowledge through Pre-training for Natural Language to Code Generation” In Annual Meeting of the Association for Computational Linguistics Online: ACL, 2020, pp. 6045–6052 DOI: 10/gn2466
  51. “A Syntactic Neural Model for General-Purpose Code Generation” arXiv, 2017 DOI: 10.48550/arXiv.1704.01696
  52. Nan Jiang, Thibaud Lutellier and Lin Tan “CURE: Code-Aware Neural Machine Translation for Automatic Program Repair” In IEEE/ACM 43rd Int’l Conf. Softw. Eng., 2021, pp. 1161–1173 DOI: 10/gk6bg9
  53. Rahul Gupta, Aditya Kanade and Shirish Shevade “Deep Reinforcement Learning for Syntactic Error Repair in Student Programs” In AAAI Conf. Artificial Intelligence 33, 2019, pp. 930–937 DOI: 10/ghkh99
  54. “TFix: Learning to Fix Coding Errors with a Text-to-Text Transformer” In Int’l Conf. Machine Learning 139 Virtual Event: PMLR, 2021, pp. 780–791
  55. “SEQUENCER: Sequence-to-Sequence Learning for End-to-End Program Repair” In IEEE Trans. Softw. Eng., 2019 DOI: 10/ggssk2
  56. “Sorting and Transforming Program Repair Ingredients via Deep Learning Code Similarities” In Int’l Conf. Softw. Analysis, Evolution and Reengineering, 2019, pp. 479–490 DOI: 10/ghbg28
  57. “CC2Vec: Distributed Representations of Code Changes” In Int’l Conf. Softw. Eng., 2020, pp. 518–529 DOI: 10/ghjj4c
  58. “Evaluating Representation Learning of Code Changes for Predicting Patch Correctness in Program Repair” In IEEE/ACM Int’l Conf. Autom. Softw. Eng., ASE ’20 New York, NY, USA: ACM, 2020, pp. 981–992 DOI: 10/gjqxpp
  59. He Ye, Matias Martinez and Martin Monperrus “Neural Program Repair with Execution-Based Backpropagation” In Int’l Conf. Softw. Eng., ICSE ’22 New York, NY, USA: ACM, 2022, pp. 1506–1518 DOI: 10/gqrnmm
  60. Zimin Chen, Steve Kommrusch and Martin Monperrus “Neural Transfer Learning for Repairing Security Vulnerabilities in C Code” In IEEE Trans. Softw. Eng. 49.1, 2023, pp. 147–165 DOI: 10/gpg8qw
  61. “On Learning Meaningful Code Changes Via Neural Machine Translation” In Int’l Conf. Softw. Eng., 2019, pp. 25–36 DOI: 10/ggssjx
  62. “A Multi-Perspective Architecture for Semantic Code Search” In Annual Meeting of the Association for Computational Linguistics Online: ACL, 2020, pp. 8563–8568 DOI: 10/gr6ngs
  63. “CoSQA: 20,000+ Web Queries for Code Search and Question Answering” arXiv, 2021 DOI: 10.48550/arXiv.2105.13239
  64. Ziyu Yao, Jayavardhan Reddy Peddamail and Huan Sun “CoaCor: Code Annotation for Code Retrieval with Reinforcement Learning” In Int’l World Wide Web Conf., WWW ’19 New York, NY, USA: ACM, 2019, pp. 2203–2214 DOI: 10/ghcqx5
  65. Geert Heyman and Tom Van Cutsem “Neural Code Search Revisited: Enhancing Code Snippet Retrieval through Natural Language Intent” arXiv, 2020 DOI: 10.48550/arXiv.2008.12193
  66. “CAST: Enhancing Code Summarization with Hierarchical Splitting and Reconstruction of Abstract Syntax Trees” arXiv, 2021 DOI: 10.48550/arXiv.2108.12987
  67. “Improved Code Summarization via a Graph Neural Network” arXiv, 2020 DOI: 10.48550/arXiv.2004.02843
  68. “Improved Automatic Summarization of Subroutines via Attention to File Context” In Int’l Conf. Mining Softw. Repositories Seoul Republic of Korea: ACM, 2020, pp. 300–310 DOI: 10/gmf67z
  69. “A Multi-Modal Transformer-based Code Summarization Approach for Smart Contracts” arXiv, 2021 DOI: 10.48550/arXiv.2103.07164
  70. “Unsupervised Translation of Programming Languages” arXiv, 2020 DOI: 10.48550/arXiv.2006.03511
  71. “CodeQA: A Question Answering Dataset for Source Code Comprehension” arXiv, 2021 DOI: 10.48550/arXiv.2109.08365
  72. “StaQC: A Systematically Mined Question-Code Dataset from Stack Overflow” In Int’l World Wide Web Conf. Lyon, France: ACM, 2018, pp. 1693–1703 DOI: 10/gkh84z
  73. Yi Li, Shaohua Wang and Tien N. Nguyen “Vulnerability Detection with Fine-Grained Interpretations” In ACM J. Meeting Eur. Softw. Eng. Conf. and Symp. Found. Softw. Eng., ESEC/FSE 2021 New York, NY, USA: ACM, 2021, pp. 292–303 DOI: 10/gmvfdr
  74. “VulBERTa: Simplified Source Code Pre-Training for Vulnerability Detection” arXiv, 2022 DOI: 10.48550/arXiv.2205.12424
  75. “DeepWukong: Statically Detecting Software Vulnerabilities Using Deep Graph Neural Network” In ACM Trans. Softw. Eng. and Methodology 30.3, 2021, pp. 1–33 DOI: 10/gk52pz
  76. “Improving Bug Detection via Context-Based Code Representation Learning and Attention-Based Neural Networks” In Proceedings of the ACM on Progr. Languages 3.OOPSLA, 2019, pp. 1–30 DOI: 10/gg3j6n
  77. “Cross-Project Transfer Representation Learning for Vulnerable Function Discovery” In IEEE Trans. Industrial Informatics 14.7, 2018, pp. 3289–3297 DOI: 10/gdwfhd
  78. “Combining Graph-Based Learning With Automated Data Collection for Code Vulnerability Detection” In IEEE Trans. Information Forensics and Security 16, 2021, pp. 1943–1958 DOI: 10/gkgf4k
  79. “DeepBugs: A Learning Approach to Name-Based Bug Detection” In Proceedings of the ACM on Progr. Languages 2.OOPSLA, 2018, pp. 1–25 DOI: 10/ggwxh2
  80. “Deep Learning Based Vulnerability Detection: Are We There Yet?” arXiv, 2020 DOI: 10.48550/arXiv.2009.07235
  81. “Automating Just-in-Time Comment Updating” In Int’l Conf. Autom. Softw. Eng. Virtual Event Australia: ACM, 2020, pp. 585–597 DOI: 10/gjqxnb
  82. “Deep Just-In-Time Inconsistency Detection Between Comments and Source Code” arXiv, 2020 DOI: 10.48550/arXiv.2010.01625
  83. “DeepCommenter: A Deep Code Comment Generation Tool with Hybrid Lexical and Syntactical Information” In ACM J. Meeting Eur. Softw. Eng. Conf. and Symp. Found. Softw. Eng. Virtual Event USA: ACM, 2020, pp. 1571–1575 DOI: 10/ghncfw
  84. “A General Path-Based Representation for Predicting Program Properties” arXiv, 2018 DOI: 10.48550/arXiv.1803.09544
  85. “Type4Py: Practical Deep Similarity Learning-Based Type Inference for Python” In Int’l Conf. Softw. Eng. New York, NY, USA: ACM, 2022, pp. 2241–2252 DOI: 10/grnbp3
  86. Veselin Raychev, Martin Vechev and Andreas Krause “Predicting Program Properties from ”Big Code”” In Symp. Princ. Prog. Lang. 50 ACM, 2015, pp. 111–124 DOI: 10/f7dbgv
  87. Rabee Sohail Malik, Jibesh Patra and Michael Pradel “NL2Type: Inferring JavaScript Function Types from Natural Language Information” In Int’l Conf. Softw. Eng. IEEE, 2019, pp. 304–315 DOI: 10/gg3j6v
  88. “Deep Learning Type Inference” In Joint Eur. Softw. Eng. Conf. and Symp. Found. Softw. Eng. New York, NY, USA: ACM, 2018, pp. 152–162 DOI: 10/gf8npn
  89. “LambdaNet: Probabilistic Type Inference Using Graph Neural Networks” arXiv, 2020 DOI: 10.48550/arXiv.2005.02161
  90. “Suggesting Accurate Method and Class Names” In Joint Eur. Softw. Eng. Conf. and Symp. Found. Softw. Eng. ACM, 2015, pp. 38–49 DOI: 10/gf8np5
  91. “ProphetNet-X: Large-Scale Pre-training Models for English, Chinese, Multi-lingual, Dialog, and Code Generation” arXiv, 2021 DOI: 10.48550/arXiv.2104.08006
  92. “CodeBERT: A Pre-Trained Model for Programming and Natural Languages” arXiv, 2020 DOI: 10.48550/arXiv.2002.08155
  93. “DOBF: A Deobfuscation Pre-Training Objective for Programming Languages” arXiv, 2021 DOI: 10.48550/arXiv.2102.07492
  94. “CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generation” arXiv, 2021 DOI: 10.48550/arXiv.2109.00859
  95. “Unified Pre-training for Program Understanding and Generation” arXiv, 2021 DOI: 10.48550/arXiv.2103.06333
  96. “Studying the Usage of Text-To-Text Transfer Transformer to Support Code-Related Tasks” arXiv, 2021 DOI: 10.48550/arXiv.2102.02017
  97. “GraphCodeBERT: Pre-training Code Representations with Data Flow” arXiv, 2021 DOI: 10.48550/arXiv.2009.08366
  98. “CodeTrans: Towards Cracking the Language of Silicon’s Code Through Self-Supervised Deep Learning and High Performance Computing” arXiv, 2021 DOI: 10.48550/arXiv.2104.02443
  99. “Global Relational Models of Source Code” In Int’l Conf. Learning Representations, 2022
  100. Nelson Tavares de Sousa and Wilhelm Hasselbring “JavaBERT: Training a Transformer-Based Model for the Java Programming Language” arXiv, 2021 DOI: 10.48550/arXiv.2110.10404
  101. “Code2vec: Learning Distributed Representations of Code” In Princ. Prog. Lang. ACM, 2019, pp. 1–29 DOI: 10/ggssk3
  102. “Big Code != Big Vocabulary: Open-Vocabulary Models for Source Code” In ACM/IEEE 42nd Int’l Conf. Softw. Eng., ICSE ’20 New York, NY, USA: ACM, 2020, pp. 1073–1085 DOI: 10/ghjj45
  103. “GraphCode2Vec: Generic Code Embedding via Lexical and Program Dependence Analyses” In Int’l Conf. Mining Softw. Repositories Pittsburgh Pennsylvania: ACM, 2022, pp. 524–536 DOI: 10/gq9r39
  104. “SPT-code: Sequence-to-Sequence Pre-Training for Learning Source Code Representations” In Int’l Conf. Softw. Eng. Pittsburgh Pennsylvania: ACM, 2022, pp. 2006–2018 DOI: 10/gqsgnq
  105. “CoTexT: Multi-task Learning with Code-Text Transformer” arXiv, 2021 DOI: 10.48550/arXiv.2105.08645
  106. “Learning and Evaluating Contextual Embedding of Source Code” In Int’l Conf. Machine Learning PMLR, 2020, pp. 5110–5121
  107. Disha Shrivastava, Hugo Larochelle and Daniel Tarlow “On-the-Fly Adaptation of Source Code Models” In NeurIPS 2020 Ws. Comp.-Assisted Prog., 2020
  108. “CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and Generation” arXiv, 2021 DOI: 10.48550/arXiv.2102.04664
  109. “Contrastive Code Representation Learning” In Conf. Empirical Methods in Natural Lang. Processing OnlinePunta Cana, Dominican Republic: ACL, 2021, pp. 5954–5971 DOI: 10/gqc5ts
  110. “Language-Agnostic Representation Learning of Source Code from Structure and Context” arXiv, 2021 DOI: 10.48550/arXiv.2103.11318
  111. Noam Yefet, Uri Alon and Eran Yahav “Adversarial Examples for Models of Code” In Proceedings of the ACM on Progr. Languages, 2020, pp. 1–30 DOI: 10/ghn4mz
  112. “Embedding Java Classes with Code2vec: Improvements from Variable Obfuscation” In Int’l Conf. Mining Softw. Repositories, MSR ’20 New York, NY, USA: ACM, 2020, pp. 243–253 DOI: 10/ghn4m9
  113. “Code2seq: Generating Sequences from Structured Representations of Code” arXiv, 2019 DOI: 10.48550/arXiv.1808.01400
  114. “Semantic Source Code Models Using Identifier Embeddings” arXiv, 2019 DOI: 10.48550/arXiv.1904.06929
  115. Loïc Lannelongue, Jason Grealey and Michael Inouye “Green Algorithms: Quantifying the Carbon Footprint of Computation” In Adv. Science 8.12, 2021, pp. 2100707 DOI: 10/gm2snp
  116. “Quantifying the Carbon Emissions of Machine Learning” arXiv, 2019 DOI: 10.48550/arXiv.1910.09700
  117. Alexandra Sasha Luccioni, Sylvain Viguier and Anne-Laure Ligozat “Estimating the Carbon Footprint of BLOOM, a 176B Parameter Language Model” arXiv, 2022 DOI: 10.48550/arXiv.2211.02001
  118. “A Holistic Assessment of the Carbon Footprint of Noor, a Very Large Arabic Language Model” In BigScience Episode #5 – Ws. Challenges & Perspectives in Creating Large Lang. Models virtual+Dublin: ACL, 2022, pp. 84–94 DOI: 10/grq5pm
  119. Lorenzo Posani, Alessio Paccoia and Marco Moschettini “The Carbon Footprint of Distributed Cloud Storage” arXiv, 2019 DOI: 10.48550/arXiv.1803.06973
  120. “Green Cloud Computing: Balancing Energy in Processing, Storage, and Transport” In Proceedings of the IEEE 99.1, 2011, pp. 149–167 DOI: 10/dcrw65
  121. Lorenz M Hilty and Wolfgang Lohmann “The Five Most Neglected Issues in ”Green IT”” In CEPIS Upgrade 12.4, 2011, pp. 5
  122. “CodeBERT: A Pre-Trained Model for Programming and Natural Languages” In Findings of the Association for Computational Linguistics: EMNLP 2020, 2020, pp. 1536–1547 DOI: 10/gj58gj
  123. “A Map of Threats to Validity of Systematic Literature Reviews in Software Engineering” In Asia-Pacific Softw. Eng. Conf., 2016, pp. 153–160 DOI: 10/gm9x9q
  124. “A Survey of App Store Analysis for Software Engineering” In IEEE Trans. Softw. Eng. 43.9, 2017, pp. 817–847 DOI: 10/grspp9
  125. Georgios Kalaitzoglou, Magiel Bruntink and Joost Visser “A Practical Model for Evaluating the Energy Efficiency of Software Applications” In Int’l Conf. ICT for Sustainability, 2014 DOI: 10/grbcv3
  126. “Improving Reproducibility in Machine Learning Research (a Report from the NeurIPS 2019 Reproducibility Program)” In J. Machine Learning Research 22.1, 2022, pp. 164:7459–164:7478
  127. “On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?” In Conf. Fairness, Accountability, and Transparency Virtual Event Canada: ACM, 2021, pp. 610–623 DOI: 10/gh677h
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Authors (3)
  1. Max Hort (10 papers)
  2. Anastasiia Grishina (8 papers)
  3. Leon Moonen (23 papers)
Citations (2)
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