mlsanityData to 7 Oct 2026

LLMs: A Game-Changer for Software Engineers?

arXiv 2411.00932 ↗First posted 1 Nov 2024cs.SE cs.AIcited by 11

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Abstract (arXiv)

Large Language Models (LLMs) like GPT-3 and GPT-4 have emerged as groundbreaking innovations with capabilities that extend far beyond traditional AI applications. These sophisticated models, trained on massive datasets, can generate human-like text, respond to complex queries, and even write and interpret code. Their potential to revolutionize software development has captivated the software engineering (SE) community, sparking debates about their transformative impact. Through a critical analysis of technical strengths, limitations, real-world case studies, and future research directions, this paper argues that LLMs are not just reshaping how software is developed but are redefining the role of developers. While challenges persist, LLMs offer unprecedented opportunities for innovation and collaboration. Early adoption of LLMs in software engineering is crucial to stay competitive in this rapidly evolving landscape. This paper serves as a guide, helping developers, organizations, and researchers understand how to harness the power of LLMs to streamline workflows and acquire the necessary skills.

Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). Builds on 38 of its 80 references that are in the map. First posted on arXiv 1 Nov 2024.

Builds on

38 of 80 references.

References · 38

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  2. 2020Language Models are Few-Shot Learnerscited by 32,199
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26 more
  1. 2023Software Testing with Large Language Models: Survey, Landscape, and Visioncited by 163
  2. 2022Multi-lingual Evaluation of Code Generation Modelscited by 158
  3. 2024What Did I Do Wrong? Quantifying LLMs' Sensitivity and Consistency to Prompt Engineeringcited by 88
  4. 2024HumanEval-XL: A Multilingual Code Generation Benchmark for Cross-lingual Natural Language Generalizationcited by 84
  5. 2023No More Manual Tests? Evaluating and Improving ChatGPT for Unit Test Generationcited by 82
  6. 2024Automated Unit Test Improvement using Large Language Models at Metacited by 73
  7. 2022Automated Repair of Programs from Large Language Modelscited by 71
  8. 2023Large Language Models for Test-Free Fault Localizationcited by 67
  9. 2024Understanding the Performance and Estimating the Cost of LLM Fine-Tuningcited by 48
  10. 2023No Need to Lift a Finger Anymore? Assessing the Quality of Code Generation by ChatGPTcited by 45
  11. 2023Navigating the Complexity of Generative AI Adoption in Software Engineeringcited by 32
  12. 2024An Empirical Study on Usage and Perceptions of LLMs in a Software Engineering Projectcited by 28
  13. 2023Refactoring Programs Using Large Language Models with Few-Shot Examplescited by 27
  14. 2024Beyond Code Generation: An Observational Study of ChatGPT Usage in Software Engineering Practicecited by 25
  15. 2024The Future of AI-Driven Software Engineeringcited by 23
  16. 2024Experimenting a New Programming Practice with LLMscited by 17
  17. 2024LangProp: A code optimization framework using Large Language Models applied to drivingcited by 17
  18. 2023Do Large Language Models Pay Similar Attention Like Human Programmers When Generating Code?cited by 13
  19. 2023ChatCoder: Chat-based Refine Requirement Improves LLMs' Code Generationcited by 13
  20. 2023Evaluating Diverse Large Language Models for Automatic and General Bug Reproductioncited by 12
  21. 2023The potential of LLMs for coding with low-resource and domain-specific programming languagescited by 11
  22. 2023Multilingual Code Co-Evolution Using Large Language Modelscited by 9
  23. 2023CoLadder: Supporting Programmers with Hierarchical Code Generation in Multi-Level Abstractioncited by 7
  24. 2024Exploring ChatGPT and its Impact on Societycited by 6
  25. 2023Frustrated with Code Quality Issues? LLMs can Help!cited by 5
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Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 7; 2026: 4.

11 papers.

  1. 2025Vibe Coding vs. Agentic Coding: Fundamentals and Practical Implications of Agentic AIcited by 39
  2. 2025Software Development Life Cycle Perspective: A Survey of Benchmarks for Code Large Language Models and Agentscited by 11
  3. 2025The Future of Generative AI in Software Engineering: A Vision from Industry and Academia in the European GENIUS Projectcited by 4
  4. 2025AI Code in the Wild: Measuring Security Risks and Ecosystem Shifts of AI-Generated Code in Modern Softwarecited by 4
  5. 2026Adaptive Hierarchical Evaluation of LLMs and SAST tools for CWE Prediction in Pythoncited by 1
  6. 2026From Junior to Senior: Allocating Agency and Navigating Professional Growth in Agentic AI-Mediated Software Engineeringcited by 1
  7. 2025Single Conversation Methodology: A Human-Centered Protocol for AI-Assisted Software Developmentcited by 0
  8. 2025On Integrating Large Language Models and Scenario-Based Programming for Improving Software Reliabilitycited by 0
  9. 2025SCoGen: Scenario-Centric Graph-Based Synthesis of Real-World Code Problemscited by 0
  10. 2026Tricky2: Towards a Benchmark for Evaluating Human and LLM Error Interactionscited by 0
  11. 2026Mitigating LLM Hallucinations through Domain-Grounded Tiered Retrievalcited by 0