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Software Development Laboratory 1

Szoftverfejlesztés laboratórium 1
A tantárgyleírás hatályossága
Hatályosság kezdete:
2026. March 21.
Hatályosság vége:
Subject name (Hungarian, English)
Szoftverfejlesztés laboratórium 1
Software Development Laboratory 1
Subject code BMEVIAUAC09
Subject type
Training Level
Course types and hours (weekly/semester)
Course type lecture tutorial laboratory
hours (weekly) 0 0 2
type (linked/independent) autonomous course
Assessment type félévközi érdemjegy
Credits 3
Subject coordinator
DR. Hideg Attila
position: adjunktus
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

-        Microsoft SQL Server programming (platform dependent SQL queries, triggers, cursors, stored procedures)

-        MongoDB database platform programming (data access methods, client-side data access using C# language, atomic data modifications, aggregation pipelines)

-        Query optimization, usage of indices (CI, NCI, TS, CISC, CISE, NCISE, NSISC, NLJ algorithms comparison, performance evaluation)

-        Programming Entity Framework (Entity Data Model, query language)

-        SQL Reporting Services (basics of Reporting Services, data sources, datasets, tables, reports, formatting, diagrams)

-        Developing multi-tier applications using REST (REST-based service development in C# using Visual Studio)

The goal of the course is to practice the material learned during Data-driven systems through laboratory exercises.

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

Exercises are to be solved during a computer laboratory class. The classes focus mainly on individual work. The exercises define a framework in which the tasks should be completed (such as, starter source code). The solutions must be submitted electronically. The solutions are software source codes and/or documentation, which the exercises clearly define.   The laboratories can be of two type. Standard computer laboratory class with a handout and aids describing the exercises. The students mainly work alone, but there is an instructor available for questions. This type of laboratory is used for the topics and technologies where the presence of a laboratory instructor is important. Certain topics are suitable for completion and submission from at home. For these, the assigned time slot of the course is used for consultation option. Attendance of the consultation may require preliminary registration so that the required number of instructors can be provided. The following topics are available for this type of laboratory: o   MongoDB database platform programming o   Programming Entity Framework

Tanulástámogató anyagok

Online források
Course; materials and exercises.

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)
Data-driven systems, Software technologies, Software laboratory 3, Softwaretechniques.
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)
Data-driven systems, Software technologies, Software laboratory 3, Softwaretechniques.
General rules
Requirements: a. During the semester:   - The student must prepare for the laboratories based on the materials of the exercises and the related materials of course Data-driven systems. - For laboratories that require attendance, the student must arrive to the laboratory class on time and must participate in the laboratory class as the instructor and the exercise handout specifies it. - For the laboratories that do not require attendance, the requirement is the completion of the exercises based on the laboratory handouts. During the completion of these laboratories, to ensure that the student did the work herself, the instructor may specify additional requirements that prove this fact. Such requirement can be, for example, the documentation of the work process through computer screenshots; frequent commits to a Git repository showing the incremental work process; or similar techniques. If the submission of the student does not adhere to these requirements, the instructor can require the student to participate in an additional class, where attendance is required, and the student must complete exercises similar to the work at home, but this time, supervised by instructors in person. - Any material produced during the laboratory (including source code, documentation, etc., as specified by the instructor) shall be submitted to the instructor as requested. The exact deadline of submission can be specified by the instructor. - Each laboratory is graded separately. The final grade is the average of the individual grades. Any laboratory that was not completed is counted with grade 1.     b. During the exam period:  None. Additional possibilities: The laboratory classes where attendance is required may be repeated upon preliminary request, depending on room capacity. Materials not submitted until the deadline may not be handed in late.
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
Recommended courses: Software techniques, Databases
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.