This course provides an in-depth exploration of machine learning techniques and their applications in software analysis. Students will learn how to leverage machine learning algorithms and methodologies to analyze code quality, detect bugs, recommend improvements, and enhance software maintenance processes.
Obiettivi Formativi
1. Understand the fundamentals of machine learning algorithms and their applications in software analysis.
2. Develop the skills necessary to apply machine learning techniques to real-world software analysis problems.
3. Learn how to evaluate and interpret machine learning models for software analysis tasks.
4. Gain hands-on experience with popular machine learning tools and frameworks for software analysis.
Metodi Didattici
Lessons will be presented through slides, papers, and possibly hands-on code sessions.
Modalità di verifica apprendimento
Student seminars
Programma del corso
1. Introduction to Machine Learning and Software Analysis
Overview of machine learning concepts and algorithms
Introduction to software analysis and its challenges
2. Preprocessing Techniques for Software Data
Data cleaning and transformation for software analysis
Feature engineering for software datasets
3. Supervised Learning for Software Analysis
Classification and regression techniques
Evaluation metrics for software analysis tasks
4. Unsupervised Learning for Software Analysis
Clustering algorithms for software data
Anomaly detection in software systems
5. Deep Learning for Software Analysis
Introduction to neural networks and deep learning architectures
Applications of deep learning in software analysis
6. Evaluation and Interpretation of Machine Learning Models
Model evaluation techniques for software analysis
Interpretability of machine learning models in software analysis
7. Hands-On Projects and Case Studies
Implementing machine learning algorithms for software analysis tasks
Analyzing real-world software datasets