Programming Paradigms and Concurrency

Teacher(s)

Stiévenart Quentin;

Language

French

Prerequisites

The prerequisite(s) for this Teaching Unit (Unité d’enseignement – UE) for the programmes/courses that offer this Teaching Unit are specified at the end of this sheet.

Main themes

Learning outcomes

At the end of this learning unit, the student is able to :

Regarding the learning outcomes of the program of Bachelor in Engineering, this course contributes to the development and the acquisition of the following learning outcomes: LO 1.1, 1.2 LO 2.3, 2.4, 2.5, 2.6, 2.7 LO 4.2, 4.3, 4.4 Given the learning outcomes of the "Bachelor in Computer Science" program, this course contributes to the development, acquisition and evaluation of the following learning outcomes: S1.I2., S1.I3, S1.I5 S2.2., S2.3, S2.4 S5.5 Students completing successfully this course will be able to specify the problems, divide them into their basic steps, and design algorithms and abstractions to solve them; choose the right programming paradigm and write a program in this paradigm to solve a problem; use formal semantics to explain the accuracy of the program; write small concurrent programs in the deterministic dataflow paradigm; think using abstractions (reason correctly on a system that includes several layers of abstractions, and define new abstractions to simplify the resolution of a problem).

Content

The goal of this course is to broaden and deepen the programming knowledge acquired in preceding courses.  The course treats the following subjects:

• The course gives a uniform framework for all programming concepts, organised as programming paradigms.
• The course gives a formal semantics and reasoning techniques for all paradigms.
• The course gives an introduction to lambda calculus as foundation of functional programming and higher-order programming.
• Higher-order programming is used as organizing principle for the construction of procedural abstractions.
• Concurrent programming is presented in two forms, namely deterministic dataflow and message-passing concurrency.
• Data abstraction is presented in its general form and with its two principal derived forms, namely object-oriented programming and abstract data types.
• Symbolic programming and algorithm design principles are used throughout the course.
• Five important programming paradigms are presented in the course: functional programming, object-oriented programming, deterministic dataflow programming, actor dataflow, and active object (multi-agent) programming.

Examples of practical applications are given for all concepts and all paradigms.

Teaching methods

Evaluation methods

The assessment consists of the following components, each identified by a variable (M1, P, F, M2, B):

• M1, Midterm exam: worth 5 points, around the 7th week. Written examination.
• P, Project: worth 5 points, during the second half of the semester. Project-based assessment. The project is mandatory and must be completed during the semester in which the course is taught. The project is assessed only once per academic year. It cannot be completed during the second examination session. An oral defense may be required as part of the project assessment.
• F, Final exam: worth 10 points. Written examination, at the end of the examination session.
• M2, Midterm retake: worth 5 points, held at the same time as the final exam. Only the higher grade between the midterm exam (M1) and its retake (M2) will be retained. Both exams will nevertheless always be graded.
• B, Practical session bonus: worth 1 point, accumulated throughout the semester. Continuous assessment based on the number of practical sessions attended by the student.

The final grade is calculated according to the following formula: min(20, max(M1, M2) + P + F + B), where M1, M2, P, F, and B are the variables defined above. The final grade therefore cannot exceed 20/20. The weighting is as follows: M1/M2 = 25%, P = 25%, F = 50%, and B = 5%, with the total capped at 100%.
In the event of a second examination session, only M2 and F are reassessed. The other grades (M1, P, B) are carried over unchanged.
The instructor reserves the right to require an oral defense from certain students in order to validate an assessment component.

Other information

Prequisite:

• Second-year programming course Informatique 2 (LEPL1402) or equivalent.

One of the objectives of the course is to enable students to develop skills and knowledge that will be useful in their future careers. The use of generative AI tools interferes with the proper acquisition of these skills and knowledge. The use of generative AI tools is therefore:

• prohibited during examinations,
• prohibited for writing code for the project or for any other code-writing activity,
• permitted only for improving the writing (spelling, grammar, minor rephrasings) of the project report, provided that (i) the student assumes full responsibility for the report submitted for assessment, and (ii) the use of generative AI is precisely documented in a generative AI use statement specifying which generative AI tools were used, for which parts of the report, and with which prompts,
• strongly discouraged during practical sessions.

All projects must include a generative AI use statement, including projects for which no generative AI tools were used. In the latter case, the statement should simply indicate that no generative AI tools were used. Any project that does not include a generative AI use statement will receive a penalty.

Online resources

LSINC1104 Moodle.

Faculty or entity

> SINC