OVERVIEW

This course explores the integration of Large Language Models (LLMs) in modern DevOps and DevSecOps practices, highlighting their role in automating software development, deployment, and security management. Students will gain both theoretical and practical knowledge on how AI-driven tools optimize coding, CI/CD pipelines, infrastructure automation, and security analysis.

AIMS AND CONTENT

LEARNING OUTCOMES

Learning state-of-the-art software engineering methods and technologies, and understanding their role in software development, deployment, and security management.

AIMS AND LEARNING OUTCOMES

By the end of the course, students will:

• Understand how LLMs can optimize software development workflows.
• Implement DevOps and DevSecOps methodologies with AI-enhanced tools.
• Develop secure, scalable, and automated software engineering solutions.

PREREQUISITES

The following knowledge is assumed to be acquired and constitutes the necessary foundations for understanding the topics of the course and the exams.

• Software Engineering
• Object Oriented programming fundamentals
• Basic concepts of Web/Mobile applications development and Networking

TEACHING METHODS

The teaching is a combination between presentation of theoretical concepts, exercises and discussions. It is dialogue-oriented and with a practical approach.

Mandatory assignments (the laboratories) which must be completed during the course will be provided to the students.

SYLLABUS/CONTENT

The course is structured into three key modules:

1. LLMs for Software Engineering – This module introduces students to state-of-the-art Large Language Models, examining their impact on code generation, testing, documentation, debugging, and static analysis. Ethical considerations and the limitations of LLMs in software engineering are also explored.
2. DevOps – Automation and Continuous Delivery – Students will gain hands-on experience with DevOps principles and tools while learning how LLMs can enhance CI/CD pipeline automation, Infrastructure as Code generation, and intelligent log analysis.
3. DevSecOps – Security in the Software Development Lifecycle – This module delves into security best practices, threat modeling, and vulnerability management, integrating LLM-powered solutions for AI-driven vulnerability detection, security policy automation, and advanced threat intelligence analysis.

RECOMMENDED READING/BIBLIOGRAPHY

Each lesson topic will be accompanied by specific references.

TEACHERS AND EXAM BOARD

Exam Board

MAURIZIO LEOTTA (President)

LESSONS

LESSONS START

According to the calendar approved by the Degree Program Board: https://corsi.unige.it/en/corsi/11964/studenti-orario

EXAMS

EXAM DESCRIPTION

The exam will consist in a written part (traditional open/closed questions, and exercises) plus the discussion of the assignments/laboratories. The final grade will be assigned by combining the evaluations of (1) the written exam, and (2) the assignments/laboratories completed during the course.

Guidelines for students with certified Specific Learning Disorders, disabilities, or other special educational needs are available at https://corsi.unige.it/en/corsi/11964/studenti-disabilita-dsa

ASSESSMENT METHODS

The acquisition of the skills foreseen by this course will be assessed via the written exam. Evaluation parameters include: the quality of exposure, the correct use of the specialist vocabulary, the ability to critically reason on the possible technical solutions to adopt and the capability to employ them in real contexts.

FURTHER INFORMATION

For further information, please refer to the course’s AulaWeb module or contact the instructor.