AI in Software Engineering

These flashcards cover key concepts and terminology related to AI's impact on software engineering, as discussed in the lecture.

AI Coding Tools

Software tools that assist developers in coding, debugging, and documentation through the application of artificial intelligence.

AI ROI

Return on investment from utilizing AI, particularly in terms of productivity and efficiency gains in software development.

The AI Paradox

A phenomenon where AI improves coding speed but does not lead to a proportional increase in software delivery by enterprises.

Code Quality Concerns

Issues related to the security and reliability of AI-generated code, including the presence of vulnerabilities.

Requirements Engineering

The process of defining, documenting, and managing software requirements.

Retrieval-Augmented Generation (RAG)

A technique where AI models enhance responses by incorporating relevant external data or context retrieved from databases.

Model Context Protocol (MCP)

A standard for connecting AI models to external tools and data sources, facilitating more integrated use of AI in applications.

LLMs

Large Language Models, which are AI models that can understand and generate human language, often used in coding and software development.

AI Literacy

The understanding of AI tools and concepts, enabling engineers to effectively orchestrate, evaluate, and direct AI systems in their workflows.

Autonomous Action

The ability of AI agents to perform tasks without human intervention, making decisions and carrying out actions based on specified goals.