From Idea to Implementation: Evaluating the Influence of Large Language Models in Software Development -- An Opinion Paper
Not on the map. Why?
Abstract (arXiv)
The introduction of transformer architecture was a turning point in Natural Language Processing (NLP). Models based on the transformer architecture such as Bidirectional Encoder Representations from Transformers (BERT) and Generative Pre-Trained Transformer (GPT) have gained widespread popularity in various applications such as software development and education. The availability of Large Language Models (LLMs) such as ChatGPT and Bard to the general public has showcased the tremendous potential of these models and encouraged their integration into various domains such as software development for tasks such as code generation, debugging, and documentation generation. In this study, opinions from 11 experts regarding their experience with LLMs for software development have been gathered and analysed to draw insights that can guide successful and responsible integration. The overall opinion of the experts is positive, with the experts identifying advantages such as increase in productivity and reduced coding time. Potential concerns and challenges such as risk of over-dependence and ethical considerations have also been highlighted.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 36 of its 79 references that are in the map. First posted on arXiv 10 Mar 2025.
Builds on
36 of 79 references.
References · 36
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
- 2020Language Models are Few-Shot Learnerscited by 32,419
- 2023GPT-4 Technical Reportcited by 18,396
- 2022Training language models to follow instructions with human feedbackcited by 15,971
- 2021Evaluating Large Language Models Trained on Codecited by 8,320
- 2020The Pile: An 800GB Dataset of Diverse Text for Language Modelingcited by 2,411
- 2024Mixtral of Expertscited by 1,823
- 2023Is Your Code Generated by ChatGPT Really Correct? Rigorous Evaluation of Large Language Models for Code Generationcited by 1,391
- 2020CodeBERT: A Pre-Trained Model for Programming and Natural Languagescited by 1,310
- 2023PaLM 2 Technical Reportcited by 1,162
- 2022BioGPT: Generative Pre-trained Transformer for Biomedical Text Generation and Miningcited by 538
- 2022Large Language Models Struggle to Learn Long-Tail Knowledgecited by 458
24 more
- 2023Large Language Models for Software Engineering: A Systematic Literature Reviewcited by 399
- 2023A Survey on Large Language Model (LLM) Security and Privacy: The Good, the Bad, and the Uglycited by 387
- 2023Do LLMs exhibit human-like response biases? A case study in survey designcited by 180
- 2023LLM Lies: Hallucinations are not Bugs, but Features as Adversarial Examplescited by 177
- 2023Structured Chain-of-Thought Prompting for Code Generationcited by 138
- 2023PentestGPT: An LLM-empowered Automatic Penetration Testing Toolcited by 97
- 2023Verbosity Bias in Preference Labeling by Large Language Modelscited by 91
- 2023A Large-Scale Survey on the Usability of AI Programming Assistants: Successes and Challengescited by 75
- 2023CodeHelp: Using Large Language Models with Guardrails for Scalable Support in Programming Classescited by 72
- 2024Disclosure and Mitigation of Gender Bias in LLMscited by 49
- 2023Summarization is (Almost) Deadcited by 44
- 2015Program Synthesis using Natural Languagecited by 41
- 2024Evaluating the Effectiveness of LLMs in Introductory Computer Science Education: A Semester-Long Field Studycited by 26
- 2019On the use of BERT for Neural Machine Translationcited by 23
- 2021NER-BERT: A Pre-trained Model for Low-Resource Entity Taggingcited by 21
- 2023Is AI the better programming partner? Human-Human Pair Programming vs. Human-AI pAIr Programmingcited by 20
- 2023LLM for Test Script Generation and Migration: Challenges, Capabilities, and Opportunitiescited by 19
- 2016A deep language model for software codecited by 14
- 2021A Survey of Natural Language Generationcited by 13
- 2024LLM-based agents for automating the enhancement of user story quality: An early reportcited by 13
- 2023LLM-based Control Code Generation using Image Recognitioncited by 10
- 2024An Empirical Study on Low-Code Programming using Traditional vs Large Language Model Supportcited by 7
- 2023The Right Prompts for the Job: Repair Code-Review Defects with Large Language Modelcited by 2
- 2025Exploring the Impact of Generative Artificial Intelligence in Education: A Thematic Analysiscited by 1
Built on by
Citing papers per year: 2025: 2; 2026: 1.
3 papers.