Objectives of Programme's Learning Outcomes

• Have acquired highly specialised and integrated knowledge and broad skills in the various disciplines of computer science, which come after those within the Bachelor's in computer science.
• Manage large-scale software development projects.
• Apply, mobilise, articulate and promote the knowledge and skills acquired in order to help lead and complete a project.
• Lead a project by mastering its complexity and taking into account the objectives, allocated resources and constraints that characterise it.
• Demonstrate independence and their ability to work alone or in teams.
• Manage research, development and innovation.
• Understand unprecedented problems in computer science and its applications.
• Organise and lead a research, development or innovation project to completion.
• Methodically research valid scientific information, lead a critical analysis, propose and argue potentially innovative solutions to targeted problems.
• Master communication techniques.
• Communicate, both orally and in writing, their findings, original proposals, knowledge and underlying principles, in a clear, structured and justified manner.
• Adapt their communication to a variety of audiences.
• Where possible, communicate in a foreign language.
• Develop and integrate a high degree of autonomy.
• Aquire new knowledge independently.
• Pursue further training and develop new skills independently.
• Develop and integrate a high degree of autonomy to evolve in new contexts.
• Apply scientific methodology.
• Critically reflect on the impact of IT in general, and on the contribution to projects.
• Demonstrate thoroughness, independence, creativity, intellectual honesty, and ethical values.

Learning Outcomes of UE

Thorough understanding of the core concepts of formal methods, mastery of the underlying mathematical frameworks, ability to go further for application on real examples.

UE Content: description and pedagogical relevance

Theory: Introduction to formal methods, Modeling systems, Linear temporal logic, Computation tree logic, Symbolic model checking, Model checking probabilistic (and quantitative) systems, Synthesis of probabilistic (and quantitative)
systems, Synthesis via game theory. Tool presentations by students.

Prior Experience

Basic notions of algorithmics, programming, complexity, logic.

Type(s) and mode(s) of Q1 UE assessment

• Oral examination - Face-to-face
• Oral presentation - Face-to-face

Q1 UE Assessment Comments

Oral exam on theory and exercises (Evaluation E1). Tool presentation by students (Evaluation E2). Final grade for UE. If an evaluation is below 8, the final grade is equal to the minimal evaluation. Otherwise, the following weights are used: E1 (80%), E2 (20%).

Method of calculating the overall mark for the Q1 UE assessment

Oral exam on theory and exercises (Evaluation E1). Tool presentation by students (Evaluation E2). Final grade for UE. If an evaluation is below 8, the final grade is equal to the minimal evaluation. Otherwise, the following weights are used: E1 (80%), E2 (20%).

Type(s) and mode(s) of Q1 UE resit assessment (BAB1)

• N/A - Néant

Q1 UE Resit Assessment Comments (BAB1)

Not applicable.

Method of calculating the overall mark for the Q1 UE resit assessment

Not applicable.

Type(s) and mode(s) of Q3 UE assessment

• Oral examination - Face-to-face
• Oral presentation - Face-to-face

Q3 UE Assessment Comments

Same rules as for Q1. Any evaluation below 10 must be passed in Q3.

Method of calculating the overall mark for the Q3 UE assessment

Same rules as for Q1. Any evaluation below 10 must be passed in Q3.