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https://arxiv.org/pdf/2608.00151
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https://arxiv.org/pdf/2608.02618
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1 Introduction

https://arxiv.org/html/2403.08299v1
Introduction As developers increasingly adopt AI assistants such as ChatGPT for their development tasks, productivity gains become evident

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https://arxiv.org/pdf/2407.00391
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https://arxiv.org/pdf/2010.15980
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https://arxiv.org/pdf/2609.02852
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https://arxiv.org/pdf/2109.12196
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https://arxiv.org/pdf/2608.03105
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https://arxiv.org/src/2410.15608
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https://arxiv.org/pdf/0804.0969
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https://arxiv.org/pdf/2608.03215
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https://arxiv.org/pdf/2608.01934
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https://arxiv.org/pdf/2006.01128
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https://arxiv.org/src/2210.03493
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https://arxiv.org/pdf/2608.02872
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https://arxiv.org/src/2608.03898
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https://arxiv.org/pdf/2407.00323
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Software Engineering

https://arxiv.org/list/cs.SE/new
In a first phase, we characterize AI-CPS TD by analyzing AI ecosystems and AI-CPS repositories, as well as interviewing developers

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https://arxiv.org/pdf/2010.15980v1
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https://arxiv.org/pdf/2205.11916v1
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https://arxiv.org/pdf/2407.02095
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https://arxiv.org/pdf/2205.11916v2
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https://arxiv.org/pdf/2608.02867
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https://arxiv.org/src/2006.03720
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https://arxiv.org/src/2006.01402
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https://arxiv.org/pdf/2407.01722
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https://arxiv.org/pdf/2205.11916v3
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https://arxiv.org/pdf/2205.11916v4
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https://arxiv.org/pdf/2608.02361
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https://arxiv.org/pdf/2006.00441
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