mlsanityData to 7 Oct 2026

Using AI Assistants in Software Development: A Qualitative Study on Security Practices and Concerns

arXiv 2405.06371 ↗First posted 10 May 2024cs.CR cs.SEcited by 27

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Abstract (arXiv)

Following the recent release of AI assistants, such as OpenAI's ChatGPT and GitHub Copilot, the software industry quickly utilized these tools for software development tasks, e.g., generating code or consulting AI for advice. While recent research has demonstrated that AI-generated code can contain security issues, how software professionals balance AI assistant usage and security remains unclear. This paper investigates how software professionals use AI assistants in secure software development, what security implications and considerations arise, and what impact they foresee on secure software development. We conducted 27 semi-structured interviews with software professionals, including software engineers, team leads, and security testers. We also reviewed 190 relevant Reddit posts and comments to gain insights into the current discourse surrounding AI assistants for software development. Our analysis of the interviews and Reddit posts finds that despite many security and quality concerns, participants widely use AI assistants for security-critical tasks, e.g., code generation, threat modeling, and vulnerability detection. Their overall mistrust leads to checking AI suggestions in similar ways to human code, although they expect improvements and, therefore, a heavier use for security tasks in the future. We conclude with recommendations for software professionals to critically check AI suggestions, AI creators to improve suggestion security and capabilities for ethical security tasks, and academic researchers to consider general-purpose AI in software development.

Key facts. Cited by 27 papers in the map (data through 7 Oct 2026). First posted on arXiv 10 May 2024.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 3; 2025: 14; 2026: 10.

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  1. 2024LLM Hallucinations in Practical Code Generation: Phenomena, Mechanism, and Mitigationcited by 92
  2. 2025Understanding Automated Program Repair Agents Through the Lens of Traceability: An Empirical Studycited by 16
  3. 2025Give LLMs a Security Course: Securing Retrieval-Augmented Code Generation via Knowledge Injectioncited by 15
  4. 2025"Your AI, My Shell": Demystifying Prompt Injection Attacks on Agentic AI Coding Editorscited by 14
  5. 2025Exploring the Security Threats of Knowledge Base Poisoning in Retrieval-Augmented Code Generationcited by 9
  6. 2025LLMs' Reshaping of People, Processes, Products, and Society in Software Development: A Comprehensive Exploration with Early Adopterscited by 8
  7. 2025Takedown: How It's Done in Modern Coding Agent Exploitscited by 6
  8. 2025Human-AI Experience in Integrated Development Environments: A Systematic Literature Reviewcited by 5
  9. 2024TaskEval: Assessing Difficulty of Code Generation Tasks for Large Language Modelscited by 3
  10. 2024Optimizing AI-Assisted Code Generationcited by 2
  11. 2025"That's another doom I haven't thought about": A User Study on AI Labels as a Safeguard Against Image-Based Misinformationcited by 2
  12. 2025"Impressively Scary:" Exploring User Perceptions and Reactions to Unraveling Machine Learning Models in Social Media Applicationscited by 1
15 more
  1. 2025Risks of AI-driven product development and strategies for their mitigationcited by 1
  2. 2025VisDocSketcher: Towards Scalable Visual Documentation with Agentic Systemscited by 1
  3. 2026Like a Hammer, It Can Build, It Can Break: Large Language Model Uses, Perceptions, and Adoption in Cybersecurity Operations on Redditcited by 1
  4. 2026Enhancing Reliability in LLM-Based Secure Code Generationcited by 1
  5. 2025What Makes Code Generation Ethically Sourced?cited by 0
  6. 2025Adobe Summit Concierge Evaluation with Human in the Loopcited by 0
  7. 2025Software Vulnerability Management in the Era of Artificial Intelligence: An Industry Perspectivecited by 0
  8. 2026A Quasi-Experimental Developer Study of Security Training in LLM-Assisted Web Application Developmentcited by 0
  9. 2026Can Open-Source LLM Agents Replace Static Application Security Testing Tools? An Empirical Assessmentcited by 0
  10. 2026Helpful or Harmful? Evaluating LLM-Assisted Vulnerability Patching via a Human Studycited by 0
  11. 2026Understanding the Impact of AI Code Assistants on Security API Usage: An Empirical Studycited by 0
  12. 2026Skills That Don't Exist: A Large-Scale Study of Hallucinated Skill Recommendation in LLM Agentscited by 0
  13. 2026From Adoption to Deployment: A Qualitative Study on AI Integration in Software Development Practicecited by 0
  14. 2026From Chasing Ghosts to Missed Attacks: Perspectives and Perceptions of SOC Practitioners on LLM Integration, Risks, and Readinesscited by 0
  15. 2026How AI Coders Discuss, Disagree, and Reach Consensus: Challenges and Opportunities for LLM-Based Qualitative Codingcited by 0