Starcoder 2 and the stack v2: The next generation

A Lozhkov, R Li, LB Allal, F Cassano… - arxiv preprint arxiv …, 2024‏ - arxiv.org
The BigCode project, an open-scientific collaboration focused on the responsible
development of Large Language Models for Code (Code LLMs), introduces StarCoder2. In …

The widening gap: The benefits and harms of generative ai for novice programmers

J Prather, BN Reeves, J Leinonen, S MacNeil… - Proceedings of the …, 2024‏ - dl.acm.org
Novice programmers often struggle through programming problem solving due to a lack of
metacognitive awareness and strategies. Previous research has shown that novices can …

Beyond the Hype: A Comprehensive Review of Current Trends in Generative AI Research, Teaching Practices, and Tools

J Prather, J Leinonen, N Kiesler… - 2024 Working Group …, 2025‏ - dl.acm.org
Generative AI (GenAI) is advancing rapidly, and the literature in computing education is
expanding almost as quickly. Initial responses to GenAI tools were mixed between panic …

Pricing and Competition for Generative AI

R Mahmood - Advances in Neural Information Processing …, 2025‏ - proceedings.neurips.cc
Compared to classical machine learning (ML) models, generative models offer a new usage
paradigm where (i) a single model can be used for many different tasks out-of-the-box;(ii) …

Benchmarks and Metrics for Evaluations of Code Generation: A Critical Review

DG Paul, H Zhu, I Bayley - 2024 IEEE International Conference …, 2024‏ - ieeexplore.ieee.org
With the rapid development of Large Language Models (LLMs), a large number of machine
learning models have been developed to assist programming tasks including the generation …

Rethinking software engineering in the foundation model era: From task-driven ai copilots to goal-driven ai pair programmers

AE Hassan, GA Oliva, D Lin, B Chen, Z Ming - arxiv preprint arxiv …, 2024‏ - arxiv.org
The advent of Foundation Models (FMs) and AI-powered copilots has transformed the
landscape of software development, offering unprecedented code completion capabilities …

Contextual api completion for unseen repositories using llms

N Nashid, T Shabani, P Alian, A Mesbah - arxiv preprint arxiv:2405.04600, 2024‏ - arxiv.org
Large language models have made substantial progress in addressing diverse code-related
tasks. However, their adoption is hindered by inconsistencies in generating output due to the …

Towards ai-native software engineering (se 3.0): A vision and a challenge roadmap

AE Hassan, GA Oliva, D Lin, B Chen, Z Ming - arxiv preprint arxiv …, 2024‏ - arxiv.org
The rise of AI-assisted software engineering (SE 2.0), powered by Foundation Models (FMs)
and FM-powered copilots, has shown promise in improving developer productivity …

Prompts are programs too! understanding how developers build software containing prompts

JT Liang, M Lin, N Rao, BA Myers - arxiv preprint arxiv:2409.12447, 2024‏ - arxiv.org
The introduction of generative pre-trained models, like GPT-4, has introduced a
phenomenon known as prompt engineering, whereby model users repeatedly write and …

Detecting code smells using chatgpt: Initial insights

LL Silva, JR Silva, JE Montandon, M Andrade… - Proceedings of the 18th …, 2024‏ - dl.acm.org
This paper presents initial insights into the effectiveness of ChatGPT in detecting code
smells in Java projects. We utilize a large dataset comprising four code smells—Blob, Data …