Paper Citation Record · LEDGER

Language Models in Software Development Tasks: An Experimental Analysis of Energy and Accuracy

As of 19 August 2026, this Paper Citation Record lists 56 of 56 outbound references and 5 inbound Pith citation observations for arXiv:2412.00329.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2412.00329 v2

Coverage vector

measured 56 of 56 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T05:32:45.250340Z

measured 61 of 61 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T04:37:28.144884Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-07-04T14:39:57.866141Z

Reference resolution

56 of 56 outbound references displayed

verified exact1
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 93b86527-02ab-4034-8edd-4c6026b34c9e · outbound

This paper cites Generative AI for software practitioners,.

Language Models in Software Development Tasks: An Experimental Analysis of Energy and Accuracy Generative AI for software practitioners,

Reference 1

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Observation 1be6cfd9-e29e-4a95-97fa-dcfb05acf384 · outbound

This paper cites AI at Work Is Here—Now Comes the Hard Part,.

Language Models in Software Development Tasks: An Experimental Analysis of Energy and Accuracy AI at Work Is Here—Now Comes the Hard Part,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:32:45.942363Z

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Observation edfed382-960a-4775-9caa-0a944f7c9b56 · outbound

This paper cites Developers get by with a little help from ai: Stack overflow knows code - assistant pulse survey results,.

Language Models in Software Development Tasks: An Experimental Analysis of Energy and Accuracy Developers get by with a little help from ai: Stack overflow knows code - assistant pulse survey results,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:32:45.932306Z

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Observation c4f9f12c-0d1e-45a4-8830-e34dfe3e0ed3 · outbound

This paper cites Research: Quantifying github copilot’s impact in the enterprise with accenture,.

Language Models in Software Development Tasks: An Experimental Analysis of Energy and Accuracy Research: Quantifying github copilot’s impact in the enterprise with accenture,

Reference 4

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Observation 55e10c98-c719-4c0a-9383-96287dc66744 · outbound

This paper cites Energy and Policy Considerations for Deep Learning in NLP.

Language Models in Software Development Tasks: An Experimental Analysis of Energy and Accuracy Energy and Policy Considerations for Deep Learning in NLP

Reference 5

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Observation ec7b5ed4-79e1-4a1a-adfa-12dc300f4ff7 · outbound

This paper cites Quantifying the Carbon Emissions of Machine Learning.

Language Models in Software Development Tasks: An Experimental Analysis of Energy and Accuracy Quantifying the Carbon Emissions of Machine Learning

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-12T05:32:45.073676Z

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Observation 94f781e5-3e65-48ff-bab3-5778b68a13af · outbound

This paper cites Estimating the Carbon Footprint of BLOOM, a 176B Parameter Language Model.

Language Models in Software Development Tasks: An Experimental Analysis of Energy and Accuracy Estimating the Carbon Footprint of BLOOM, a 176B Parameter Language Model

Reference 7

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Observation a62576a7-f0ee-4f06-b0fe-0917c69b8ba4 · outbound

This paper cites The carbon footprint of machine learning training will plateau, then shrink,.

Language Models in Software Development Tasks: An Experimental Analysis of Energy and Accuracy The carbon footprint of machine learning training will plateau, then shrink,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:32:45.910538Z

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Observation 5be544c8-cc74-4c26-b689-055b2e2791fc · outbound

This paper cites A Survey of Large Language Models for Code: Evolution, Benchmarking, and Future Trends.

Language Models in Software Development Tasks: An Experimental Analysis of Energy and Accuracy A Survey of Large Language Models for Code: Evolution, Benchmarking, and Future Trends

Reference 9

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Observation ffc2b24f-87d0-4942-88a5-2f5b32567049 · outbound

This paper cites A Survey on Large Language Models for Code Generation.

Language Models in Software Development Tasks: An Experimental Analysis of Energy and Accuracy A Survey on Large Language Models for Code Generation

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-12T05:32:45.089335Z

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Observation 6eae1baf-ee0e-4004-8f5f-9a8db7e87158 · outbound

This paper cites Replication package,.

Language Models in Software Development Tasks: An Experimental Analysis of Energy and Accuracy Replication package,

Reference 11

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Observation 6e4f90e7-7b29-4b12-92bc-5a3edb9bb1ee · outbound

This paper cites Attention is all you need,.

Language Models in Software Development Tasks: An Experimental Analysis of Energy and Accuracy Attention is all you need,

Reference 12

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Observation 23973b26-212e-43dc-8f8a-1e3e839c40a9 · outbound

This paper cites History, development, and principles of large language models: an introductory survey,.

Language Models in Software Development Tasks: An Experimental Analysis of Energy and Accuracy History, development, and principles of large language models: an introductory survey,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:32:45.892659Z

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Observation 3df38071-4f7c-4046-9aa5-1f8dd3d813b6 · outbound

This paper cites A comprehensive evaluation of quantization strategies for large language models,.

Language Models in Software Development Tasks: An Experimental Analysis of Energy and Accuracy A comprehensive evaluation of quantization strategies for large language models,

Reference 14

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Observation ef078453-2429-44a7-968b-7dda9494dcb2 · outbound

This paper cites A survey of low-bit large language models: Basics, systems, and algorithms,.

Language Models in Software Development Tasks: An Experimental Analysis of Energy and Accuracy A survey of low-bit large language models: Basics, systems, and algorithms,

Reference 15

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Observation 29ca76bc-1bc1-473f-9401-200ec84f3efa · outbound

This paper cites Benchmarking emerging deep learning quantization methods for energy efficiency,.

Language Models in Software Development Tasks: An Experimental Analysis of Energy and Accuracy Benchmarking emerging deep learning quantization methods for energy efficiency,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:32:45.871792Z

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Observation 632713b2-d524-4082-bee7-59a66eaf4d3e · outbound

This paper cites A systematic literature review on the use of deep learning in software engineering research,.

Language Models in Software Development Tasks: An Experimental Analysis of Energy and Accuracy A systematic literature review on the use of deep learning in software engineering research,

Reference 17

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Observation 788fddbe-b521-4b34-8048-a7af6248b072 · outbound

This paper cites Deepbugs: A learning approach to name-based bug detection,.

Language Models in Software Development Tasks: An Experimental Analysis of Energy and Accuracy Deepbugs: A learning approach to name-based bug detection,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:32:45.850234Z

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Observation 7a1433d6-7a6e-4c36-be69-b04591ff47fd · outbound

This paper cites Evaluating the effectiveness of deep learning models for foundational program analysis tasks,.

Language Models in Software Development Tasks: An Experimental Analysis of Energy and Accuracy Evaluating the effectiveness of deep learning models for foundational program analysis tasks,

Reference 19

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Observation dbaeffb3-ba5f-4414-87f5-2ebe98fc6efb · outbound

This paper cites OctoPack: Instruction Tuning Code Large Language Models.

Language Models in Software Development Tasks: An Experimental Analysis of Energy and Accuracy OctoPack: Instruction Tuning Code Large Language Models

Reference 20

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Observation c9f7bed6-2d14-4513-9970-e03fe3b38256 · outbound

This paper cites Evaluating Large Language Models Trained on Code.

Language Models in Software Development Tasks: An Experimental Analysis of Energy and Accuracy Evaluating Large Language Models Trained on Code

Reference 21

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Observation 65d9f6c2-3b6e-4bd3-9ddb-561f72a4e408 · outbound

This paper cites Code Llama: Open Foundation Models for Code.

Language Models in Software Development Tasks: An Experimental Analysis of Energy and Accuracy Code Llama: Open Foundation Models for Code

Reference 22

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Observation ee3f2262-a98d-4ca4-bb54-15ce281238b2 · outbound

This paper cites Starcoder: may the source be with you!.

Language Models in Software Development Tasks: An Experimental Analysis of Energy and Accuracy Starcoder: may the source be with you!

Reference 23

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Observation fd1144a1-fb80-41a8-bb34-7e3487ce3884 · outbound

This paper cites Pydex: Repairing bugs in introductory python assign- ments using llms,.

Language Models in Software Development Tasks: An Experimental Analysis of Energy and Accuracy Pydex: Repairing bugs in introductory python assign- ments using llms,

Reference 24

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Observation ea50bf7c-7abf-431f-a643-5b6f60c489ca · outbound

This paper cites Carbontracker: Tracking and Predicting the Carbon Footprint of Training Deep Learning Models.

Language Models in Software Development Tasks: An Experimental Analysis of Energy and Accuracy Carbontracker: Tracking and Predicting the Carbon Footprint of Training Deep Learning Models

Reference 25

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Observation a8725c4f-f40a-421d-b6f9-12a872e5427a · outbound

This paper cites Evaluating the energy efficiency of deep convolutional neural networks on cpus and gpus,.

Language Models in Software Development Tasks: An Experimental Analysis of Energy and Accuracy Evaluating the energy efficiency of deep convolutional neural networks on cpus and gpus,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:32:45.813804Z

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Observation c2ed3283-aefa-4822-821c-ca0af518379f · outbound

This paper cites Uncov- ering energy-efficient practices in deep learning training: Preliminary steps towards green AI,.

Language Models in Software Development Tasks: An Experimental Analysis of Energy and Accuracy Uncov- ering energy-efficient practices in deep learning training: Preliminary steps towards green AI,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:32:45.804732Z

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Observation d4dc5642-9e66-4a6f-9a03-6b0fb69c4bc6 · outbound

This paper cites The Computational Limits of Deep Learning.

Language Models in Software Development Tasks: An Experimental Analysis of Energy and Accuracy The Computational Limits of Deep Learning

Reference 28

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Observation 9bf39659-84ae-44a1-b234-418d00c0ab0c · outbound

This paper cites Great power, great responsibility: Recommendations for reducing en- ergy for training language models,.

Language Models in Software Development Tasks: An Experimental Analysis of Energy and Accuracy Great power, great responsibility: Recommendations for reducing en- ergy for training language models,

Reference 29

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Observation 0a003c79-c45e-42c3-92ca-6974a5b1b473 · outbound

This paper cites Compute and Energy Consumption Trends in Deep Learning Inference.

Language Models in Software Development Tasks: An Experimental Analysis of Energy and Accuracy Compute and Energy Consumption Trends in Deep Learning Inference

Reference 30

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Observation 65548e7a-b14a-405e-b847-83a65acd9a1b · outbound

This paper cites From words to watts: Benchmarking the energy costs of large language model inference,.

Language Models in Software Development Tasks: An Experimental Analysis of Energy and Accuracy From words to watts: Benchmarking the energy costs of large language model inference,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:32:45.785749Z

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Observation 3763079a-0378-4409-aa75-d75ac24d87b4 · outbound

This paper cites Power hungry processing: Watts driving the cost of AI deployment?.

Language Models in Software Development Tasks: An Experimental Analysis of Energy and Accuracy Power hungry processing: Watts driving the cost of AI deployment?

Reference 32

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Observation 59050bcf-c1df-472b-92e1-6e85889e0262 · outbound

This paper cites So you want your private LLM at home? A survey and benchmark of methods for efficient gpts,.

Language Models in Software Development Tasks: An Experimental Analysis of Energy and Accuracy So you want your private LLM at home? A survey and benchmark of methods for efficient gpts,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:32:45.766726Z

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Observation 03c15bf4-05b8-4c4f-acc1-4e5af21a2efe · outbound

This paper cites Hugging Face: The AI Community Building the Future,.

Language Models in Software Development Tasks: An Experimental Analysis of Energy and Accuracy Hugging Face: The AI Community Building the Future,

Reference 34

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Observation c3a32fba-1fb4-45fe-8eff-10e265c8bfc9 · outbound

This paper cites Ollama Model Library,.

Language Models in Software Development Tasks: An Experimental Analysis of Energy and Accuracy Ollama Model Library,

Reference 35

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Observation 99bf57f8-52c9-4a44-84ab-d5fc0d717b7f · outbound

This paper cites GGUF Format Documentation,.

Language Models in Software Development Tasks: An Experimental Analysis of Energy and Accuracy GGUF Format Documentation,

Reference 36

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Observation 9b0d145a-68e4-4280-961f-eccb43bb1828 · outbound

This paper cites llama.cpp: A C++ Implementation of LLaMA Model,.

Language Models in Software Development Tasks: An Experimental Analysis of Energy and Accuracy llama.cpp: A C++ Implementation of LLaMA Model,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:32:45.728223Z

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Observation 907abd44-c345-4584-a558-84b190f80469 · outbound

This paper cites EvalPlus Leaderboard,.

Language Models in Software Development Tasks: An Experimental Analysis of Energy and Accuracy EvalPlus Leaderboard,

Reference 38

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Observation 99b24e32-ce22-49da-b9a5-508566e073a6 · outbound

This paper cites Is your code generated by chatgpt really correct? rigorous evaluation of large language models for code generation,.

Language Models in Software Development Tasks: An Experimental Analysis of Energy and Accuracy Is your code generated by chatgpt really correct? rigorous evaluation of large language models for code generation,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:32:45.707945Z

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Observation 424e2b6a-cdf2-46d6-a21c-3ed09544f486 · outbound

This paper cites How to use local llms,.

Language Models in Software Development Tasks: An Experimental Analysis of Energy and Accuracy How to use local llms,

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:32:45.696981Z

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Observation 864f5c5b-82bf-45b0-ac89-9710affe9feb · outbound

This paper cites Available: https://python .langchain.com/v0.2/docs/how to/local llms/#inference.

Language Models in Software Development Tasks: An Experimental Analysis of Energy and Accuracy Available: https://python .langchain.com/v0.2/docs/how to/local llms/#inference

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:32:45.686517Z

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Observation 2bb803d7-da7b-4707-a646-5127a65e633d · outbound

This paper cites Green AI: a preliminary empirical study on energy consumption in DL models across different runtime infrastruc- tures,.

Language Models in Software Development Tasks: An Experimental Analysis of Energy and Accuracy Green AI: a preliminary empirical study on energy consumption in DL models across different runtime infrastruc- tures,

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:32:45.676233Z

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Observation f14d6d94-39f2-4d88-9058-33e7eccd798a · outbound

This paper cites Measuring and improving the energy efficiency of large language models inference,.

Language Models in Software Development Tasks: An Experimental Analysis of Energy and Accuracy Measuring and improving the energy efficiency of large language models inference,

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:32:45.665810Z

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Observation 84855dd1-dafa-4f30-9a84-1eb2506b07ce · outbound

This paper cites Estimating the Energy Footprint of Software Systems: a Primer.

Language Models in Software Development Tasks: An Experimental Analysis of Energy and Accuracy Estimating the Energy Footprint of Software Systems: a Primer

Reference 44

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Language Models in Software Development Tasks: An Experimental Analysis of Energy and Accuracy NVIDIA Management Library (NVML),

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:32:45.655376Z

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Observation f9024dd8-6d5f-43e1-a0bb-ebe8092bdab5 · outbound

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Language Models in Software Development Tasks: An Experimental Analysis of Energy and Accuracy pynvml: Python bindings for NVML,

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:32:45.645273Z

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Observation ad6faf85-29a2-43f0-a91e-9c7277fee205 · outbound

This paper cites RAPL in action: Experiences in using RAPL for power measurements,.

Language Models in Software Development Tasks: An Experimental Analysis of Energy and Accuracy RAPL in action: Experiences in using RAPL for power measurements,

Reference 47

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Source-reported events for the cited work

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Observation 9daacd35-e346-4e17-a866-4dad1ba57d15 · outbound

This paper cites pyRAPL: Python library for measuring energy consumption with RAPL,.

Language Models in Software Development Tasks: An Experimental Analysis of Energy and Accuracy pyRAPL: Python library for measuring energy consumption with RAPL,

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:32:45.629145Z

Source-reported events for the cited work

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Observation 00f5c4bc-df92-4645-b348-11e2c1ce4e9e · outbound

This paper cites Running Average Power Limit (RAPL) Energy Reporting,.

Language Models in Software Development Tasks: An Experimental Analysis of Energy and Accuracy Running Average Power Limit (RAPL) Energy Reporting,

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:32:45.618331Z

Source-reported events for the cited work

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Observation 36731958-ffba-4531-9e48-7c6b7f0c2d11 · outbound

This paper cites NVIDIA System Management Interface (nvidia- smi),.

Language Models in Software Development Tasks: An Experimental Analysis of Energy and Accuracy NVIDIA System Management Interface (nvidia- smi),

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:32:45.607699Z

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Observation 769cf178-c550-4bdd-9a75-ba67de071acb · outbound

This paper cites StarCoder 2 and The Stack v2: The Next Generation.

Language Models in Software Development Tasks: An Experimental Analysis of Energy and Accuracy StarCoder 2 and The Stack v2: The Next Generation

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-12T05:32:45.232063Z

Source-reported events for the cited work

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Observation c75d072e-65d4-421c-b4d1-5e57decb6552 · outbound

This paper cites Mercury: A Code Efficiency Benchmark for Code Large Language Models.

Language Models in Software Development Tasks: An Experimental Analysis of Energy and Accuracy Mercury: A Code Efficiency Benchmark for Code Large Language Models

Reference 52

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Observation 73d84626-4f6b-4e58-8615-87b37aa3e72e · outbound

This paper cites Magicoder: Em- powering code generation with oss-instruct,.

Language Models in Software Development Tasks: An Experimental Analysis of Energy and Accuracy Magicoder: Em- powering code generation with oss-instruct,

Reference 53

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Observation dfa9beb2-b6b5-4f1e-b0aa-8317f0beb193 · outbound

This paper cites Alpaca: A strong, replicable instruction- following model,.

Language Models in Software Development Tasks: An Experimental Analysis of Energy and Accuracy Alpaca: A strong, replicable instruction- following model,

Reference 54

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Observation 004312e9-39b0-40ad-848d-1db5e78ba150 · outbound

This paper cites Large Language Models as Test Case Generators: Performance Evaluation and Enhancement.

Language Models in Software Development Tasks: An Experimental Analysis of Energy and Accuracy Large Language Models as Test Case Generators: Performance Evaluation and Enhancement

Reference 55

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Observation ffbfc4a7-b577-4ba0-9be8-94acb343345f · outbound

This paper cites Coverage.py: The code coverage tool for Python.

Language Models in Software Development Tasks: An Experimental Analysis of Energy and Accuracy Coverage.py: The code coverage tool for Python

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:32:45.574416Z

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Pith citing papers

Observation c3f7df00-6c4a-4747-b85b-940fa52c4ab8 · inbound

Aggregating empirical evidence from data strategy studies: a case on model quantization cites this paper.

Aggregating empirical evidence from data strategy studies: a case on model quantization Language Models in Software Development Tasks: An Experimental Analysis of Energy and Accuracy

Reference 25

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Greening AI-enabled Systems with Software Engineering: A Research Agenda for Environmentally Sustainable AI Practices cites this paper.

Greening AI-enabled Systems with Software Engineering: A Research Agenda for Environmentally Sustainable AI Practices Language Models in Software Development Tasks: An Experimental Analysis of Energy and Accuracy

Reference 1

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Which Prompting Technique Should I Use? An Empirical Investigation of Prompting Techniques for Software Engineering Tasks cites this paper.

Which Prompting Technique Should I Use? An Empirical Investigation of Prompting Techniques for Software Engineering Tasks Language Models in Software Development Tasks: An Experimental Analysis of Energy and Accuracy

Reference 26

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Smaller Models, Unexpected Costs: Trade-offs in LLM Quantization for Automated Program Repair cites this paper.

Smaller Models, Unexpected Costs: Trade-offs in LLM Quantization for Automated Program Repair Language Models in Software Development Tasks: An Experimental Analysis of Energy and Accuracy

Reference 13

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Observation cabef859-6383-46d0-9582-e8619f79dd3e · inbound

Smaller Models, Unexpected Costs: Trade-offs in LLM Quantization for Automated Program Repair cites this paper.

Smaller Models, Unexpected Costs: Trade-offs in LLM Quantization for Automated Program Repair Language Models in Software Development Tasks: An Experimental Analysis of Energy and Accuracy

Reference 13

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