ECTS - - Software Engineering Master of Science with Thesis

Compulsory Departmental Courses

MDES602 - Research Methods and Ethics (3 + 0) 10

This course provides students with knowledge on research methods, data collection principles, and practices. Within this framework, it addresses the ethical rules to be followed in scientific research and publishing processes, along with ethical responsibilities throughout data collection, analysis, and reporting phases.

SE521 - Software Engineering Principles and Practices (3 + 0) 10

Introduction to software engineering. Software process models. Agile development. Requirements modeling with UML. Architectural, component-level and user interface design. Quality concepts. Security engineering. Configuration management. Estimation for software projects. Risk management. Project management concepts.

SE523 - Theory of Software Quality (3 + 0) 10

Introduction to software quality and assurance. Components of software quality assurance. Configuration management. Reviews, inspection and audits. Software testing strategies and techniques. Software quality standards, certification and assessment.

SE889 - Graduation Seminar (0 + 0) 10

Each student in MS Program with Thesis is expected to give a presentation on his/her thesis work and attend the seminars conducted by the other students and academic staff.

SE897 - Graduation Thesis (0 + 0) 60

Graduation Thesis

Elective Courses

SE544 - Cognitive Aspects of Software Engineering (3 + 0) 5

Introduction to cognitive science and its methods; cognitive processes related to software engineering (memory, expertise, attention, decision making and problem solving, team cognition); basic experimental design; case studies on cognitive aspects of software engineering research.

SE559 - Software Testing and Maintenance (3 + 0) 5

Fundamentals of testing; testing through software lifecycle; lifecycle of testing; static testing techniques; test design techniques; defect management.

SE573 - Applied Machine Learning in Data Analytics (3 + 0) 5

Data statistics; linear discriminant analysis; decision trees; artificial neural networks; Bayesian learning; distance measures; instance-based and reinforcement learning; clustering; regression; support vector machines.