# Ways of Applying Artificial Intelligence in Software Engineering

arXiv: https://arxiv.org/abs/1802.02033 · HTML: https://mlsanity.com/paper/1802.02033/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Feb 2018.

## Built on by (most cited first, 7 of 7)

- [Towards Autonomous Testing Agents via Conversational Large Language Models](https://mlsanity.com/paper/2306.05152/) (2023)
- [Quality Assurance Challenges for Machine Learning Software Applications During Software Development Life Cycle Phases](https://mlsanity.com/paper/2105.01195/) (2021)
- [Artificial Intelligence Impact On The Labour Force -- Searching For The Analytical Skills Of The Future Software Engineers](https://mlsanity.com/paper/2302.13229/) (2023)
- [Leveraging Artificial Intelligence on Binary Code Comprehension](https://mlsanity.com/paper/2210.05103/) (2022)
- [From Code Generation to Software Testing: AI Copilot with Context-Based RAG](https://mlsanity.com/paper/2504.01866/) (2025)
- [The Semi-Executable Stack: Agentic Software Engineering and the Expanding Scope of SE](https://mlsanity.com/paper/2604.15468/) (2026)
- [Empirical Software Engineering TerraProbe: A Layered-Oracle Framework for Detecting Deceptive Fixes in LLM-Assisted Terraform](https://mlsanity.com/paper/2606.26590/) (2026)

## Abstract (arXiv)

As Artificial Intelligence (AI) techniques have become more powerful and easier to use they are increasingly deployed as key components of modern software systems. While this enables new functionality and often allows better adaptation to user needs it also creates additional problems for software engineers and exposes companies to new risks. Some work has been done to better understand the interaction between Software Engineering and AI but we lack methods to classify ways of applying AI in software systems and to analyse and understand the risks this poses. Only by doing so can we devise tools and solutions to help mitigate them. This paper presents the AI in SE Application Levels (AI-SEAL) taxonomy that categorises applications according to their point of AI application, the type of AI technology used and the automation level allowed. We show the usefulness of this taxonomy by classifying 15 papers from previous editions of the RAISE workshop. Results show that the taxonomy allows classification of distinct AI applications and provides insights concerning the risks associated with them. We argue that this will be important for companies in deciding how to apply AI in their software applications and to create strategies for its use.