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Model-based Software Development Laboratory 

Modellalapú szoftverfejlesztés labor
A tantárgyleírás hatályossága
Hatályosság kezdete:
2026. March 21.
Hatályosság vége:
Subject name (Hungarian, English)
Modellalapú szoftverfejlesztés labor
Model-based Software Development Laboratory 
Subject code BMEVIAUMA23
Subject type
Training Level
Course types and hours (weekly/semester)
Course type lecture tutorial laboratory
hours (weekly) 0 0 3
type (linked/independent) autonomous course
Assessment type félévközi érdemjegy
Credits 5
Subject coordinator
DR. Mezei Gergely
position: egyetemi docens
Responsible department
Automatizálási és Alkalmazott Informatikai Tanszék
Faculty Villamosmérnöki és Informatikai Kar
Subject website
Primary curriculum type
Direct prerequisites – Strong prerequisite none
Direct prerequisites – Weak prerequisite none
Direct prerequisites – Parallel prerequisite none
Direct prerequisites – Milestone prerequisite none
Direct prerequisites – Exclusion none

Objectives

Programme
Students go through the process of creating and processing a domain-specific language and the models that can be made from it.

1. Lab: getting to know the field, developing the EMF-based metamodel
2. Individual task: creating an Xtext-based text editor for the metamodel (2 times)
3. Task presentation: Xtext
4. Individual task: Model processing using graph transformation (2 times)
5. Task presentation: Graph transformation
6. Individual task: Additional modules, Blockly and ANTLR (2 times)
7. Assignment presentation: Modules

The subject has a 10x4-hour time slot, in which attendance sessions also take place. The 1st lab can be completed synchronously by attendance or asynchronously online (based on the published supporting materials). The results of the labs are evaluated according to the deadline announced at the beginning of the semester.

The individual project tasks correspond to 2 occasions in terms of their size. The students perform these tasks at home, but the instructors provide the opportunity for personal consultation during the time slot of the subject. The results of the individual project tasks must be presented during the personal task presentations (also in the time slot). The solutions are evaluated during the presentation.
The purpose of the course is for students to learn to apply the theoretical knowledge acquired in the field of model-based software development in practice.

Learning outcomes

Ez a tantárgy a KKK rendeletben meghatározott, következő kompetenciák fejlesztését szolgálja:

Knowledge

No learning outcomes recorded.

Skills

No learning outcomes recorded.

Attitudes

No learning outcomes recorded.

Autonomy and responsibility

No learning outcomes recorded.

Oktatási módszertan

Lab

Tanulástámogató anyagok

Online források
Krysztof Czarnecki, Ulrich Eisenecker, Generative Programming: Methods, Tools, and Applications, Addison-Wesley, 2000. ; Steven Kelly, Juha-Pekka Tolvanen, Domain-Specific Modeling: Enabling Full Code Generation, Wiley-IEEE Computer Society Press, 2008. ; Martin Fowler, Domain-Specific Languages, Addison-Wesley Professional, 2010 

Recommended preliminary knowledge for completing the subject

Knowledge type competencies
(azon előzetes ismeretek összessége, amelyek megléte nem kötelező, de a tantárgy eredményes teljesítését nagyban elősegíti)
Compilers, software modeling, programming
Skill type competencies
(azon előzetes képességek és készségek összessége, amelyek megléte nem kötelező, de a tantárgy eredményes teljesítését nagyban elősegíti)
nincs
Recommended (non-compulsory) preliminary competencies
(azon ajánlott (nem kötelező) előzetesen megszerzendő kompetenciák összessége, amelyek jelentősen hozzájárulnak a tantárgy eredményes teljesítéséhez)
Compilers, software modeling, programming
General rules
Requirements: Solving the task of the first lab at a sufficient level (production of the required model), the result of the lab is not included in the end-of-semester grade. In the remainder of the semester, the condition for a satisfactory grade is to present at least a sufficient solution in all three topics. If this condition is met, the midterm grade is the average of the grades received on the three assignment presentations. Additional possibilities: The first task cannot be re-taken. Each of the three task presentations can be retaken once according to the schedule announced at the beginning of the semester.
Assessment methods
In-term assessments

No detailed assessments provided.

Weight of in-term assessments

No weights provided.

Exam-period assessments

No detailed assessments provided.

Weight of exam elements

No weights provided.

Grade calculation

No grade thresholds provided.

Attendance requirements

No attendance requirements provided.

Rules for retake and resubmission

Not provided.

Short description

Not provided.

Detailed description

Not provided.

Recommended courses

Not provided.

Workload to complete the subject

No workload breakdown provided.

Validity of subject requirements
Requirements valid from:
Requirements valid until:
Curriculum placement

No curriculum placements recorded for this subject version.