## 2 Background and Related Work
### Software Engineering for AI (SE4 AI) and Software Engineering for Machine Learning (SE4 ML)  
The development of AI-based systems and ML-based software applications presents unique challenges that extend beyond the scope of traditional software engineering. Unlike conventional systems that follow explicit programmatic rules, AI systems rely on data-driven inference, leading to increased uncertainty, evolving requirements, and life-cycle complexity [1]. These differences have motivated the emergence of Software Engineering for AI (SE4 AI) and Software Engineering for Machine Learning (SE4 ML) as dedicated subfields aimed at adapting and extending classical engineering practices to the AI/ML context.  
SE4 AI and SE4 ML propose structured methodologies, best practices, and toolsets to address the specific needs of AI development [85], including robustness, maintainability, explainability, and ethical considerations. Several key differences distinguish AI/ML system engineering from traditional software development:  
* Requirements Engineering: In AI/ML systems, requirements are not fully specified up-front but evolve through iterative learning cycles based on data and model feedback [86]. Techniques such as incremental requirement refinement and data-centric requirement analysis are essential.
* Software Design and Architecture: Managing complex data pipelines, model versioning, deployment workflows, and continuous retraining requires specialized design patterns tailored for AI systems [23]. To prevent the accumulation of technical debt, AI/ML architectures should prioritize modularity, scalability, and the ability to adapt across evolving data and model requirements.
* Testing and Quality Assurance: Traditional software testing approaches fall short in the face of non-deterministic AI behavior. Ensuring system reliability demands the use of AI-specific testing techniques, including data validation, model evaluation, metamorphic testing, and uncertainty quantification [23].
* Deployment and Maintenance: Addressing real-world phenomena such as data drift, model degradation, and evolving user feedback requires continuous monitoring, automated retraining pipelines, and adaptive update mechanisms. MLOps frameworks offer the foundational infrastructure to support these ongoing lifecycle management needs efficiently [87].
* Ethical and Social Considerations: The development of AI systems raises important ethical concerns, including algorithmic bias, lack of transparency in decision-making, and fairness in outcomes. Responsible AI frameworks advocate for embedding ethical safeguards throughout the design, training, and deployment phases to ensure systems align with broader societal values [79].
* Training and Skill Development: Contemporary SE4 AI curricula emphasize the need for interdisciplinary competencies that combine technical software engineering skills with principles from AI governance, ethics, and human-centered design [88].  
By incorporating these evolving engineering practices, SE4 AI and SE4 ML aim to improve the robustness, scalability, and long-term maintainability of AI/ML systems, while also mitigating forms of technical debt that are unique to data-driven and adaptive applications.