Advancements in large language models (LLMs) have enhanced their ability to handle ambiguous user instructions. However, effective prompt patterns remain crucial for usability and comprehension. This article presents a taxonomy of prompt engineering patterns for software engineering. It is based on a systematic literature review that was conducted in early 2023, when LLMs still faced significant limitations in context length and inference capabilities. Our study explores techniques that enhance the usability and reliability of LLMs, emphasizing the ongoing importance of well-designed prompts in optimizing task performance. Our findings highlight the critical role of prompt patterns in maximizing LLM's potential, even as their capabilities continue to evolve.

To address the breadth of AI quality risks including security ones, we propose the 'AI Engineering Continuum' as a comprehensive framework consisting of the following five dimensions necessary from the holistic engineering point of view: AI layer continuum, AI process continuum, AI computing continuum, AI automation continuum, and AI quality continuum. The AI engineering continuum is intended to be used to evaluate the adequacy of AI engineering projects and identify weaknesses that should be improved and aligned with the dimensions of the framework. Furthermore, we discuss necessary engineering features and foundations needed to efficiently and effectively raise each dimension of the framework and demonstrate some of their preliminary implementations, including our past achievements. © 2024 IEEE.

We propose a conceptual framework, named "AI Security Continuum,"consisting of dimensions to deal with challenges of the breadth of the AI security risk sustainably and systematically under the emerging context of the computing continuum as well as continuous engineering. The dimensions identified are the continuum in the AI computing environment, the continuum in technical activities for AI, the continuum in layers in the overall architecture, including AI, the level of AI automation, and the level of AI security measures. We also prospect an engineering foundation that can efficiently and effectively raise each dimension. © 2024 Copyright held by the owner/author(s).

As machine learning expands to various domains, the demand for reusable solutions to similar problems increases. Machine learning design patterns are reusable solutions to design problems of machine learning applications. They can significantly enhance programmers' productivity in programming that requires machine learning algorithms. Given the critical role of machine learning design patterns, the automated detection of them becomes equally vital. However, identifying design patterns can be time-consuming and error-prone. We propose an approach to detect their occurrences in Python files. Our approach uses an Abstract Syntax Tree (AST) of Python files to build a corpus of data and train a refined Text-CNN model to automatically identify machine learning design patterns. We empirically validate our approach by conducting an exploratory study to detect four common machine learning design patterns: Embedding, Multilabel, Feature Cross, and Hashed Feature. We manually label 450 Python code files containing these design patterns from repositories of projects in GitHub. Our approach achieves accuracy values ranging from 80 % to 92% for each of the four patterns. © 2023 IEEE.